Overview

Dataset statistics

Number of variables41
Number of observations230436
Missing cells1593261
Missing cells (%)16.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.1 MiB
Average record size in memory187.2 B

Variable types

Numeric16
DateTime5
Categorical20

Alerts

S_sprache has constant value "Deutsch" Constant
Kommentar has a high cardinality: 60552 distinct values High cardinality
ft_tu has a high cardinality: 55 distinct values High cardinality
ft_vm has a high cardinality: 18455 distinct values High cardinality
fg_startort has a high cardinality: 12200 distinct values High cardinality
fg_zielort has a high cardinality: 12048 distinct values High cardinality
ft_startort has a high cardinality: 6271 distinct values High cardinality
ft_zielort has a high cardinality: 5373 distinct values High cardinality
participant_id is highly correlated with device_typeHigh correlation
wime_personal is highly correlated with wime_komfort and 7 other fieldsHigh correlation
wime_komfort is highly correlated with wime_personal and 7 other fieldsHigh correlation
wime_sauberkeit is highly correlated with wime_personal and 6 other fieldsHigh correlation
wime_puenktlich is highly correlated with wime_personal and 6 other fieldsHigh correlation
wime_platzangebot is highly correlated with wime_personal and 6 other fieldsHigh correlation
wime_gesamtzuf is highly correlated with wime_personal and 7 other fieldsHigh correlation
wime_preis_leistung is highly correlated with wime_personal and 5 other fieldsHigh correlation
wime_fahrplan is highly correlated with wime_personal and 6 other fieldsHigh correlation
wime_oes_fahrt is highly correlated with wime_personal and 3 other fieldsHigh correlation
S_alter is highly correlated with S_spracheHigh correlation
u_preis is highly correlated with S_spracheHigh correlation
ft_startort_uic is highly correlated with ft_tu and 2 other fieldsHigh correlation
ft_zielort_uic is highly correlated with ft_tu and 2 other fieldsHigh correlation
fg_startort_uic is highly correlated with ft_startort_uic and 1 other fieldsHigh correlation
fg_zielort_uic is highly correlated with ft_tu and 2 other fieldsHigh correlation
S_sprache is highly correlated with S_AB3_HTA and 12 other fieldsHigh correlation
S_sex is highly correlated with S_spracheHigh correlation
S_wohnsitz is highly correlated with S_spracheHigh correlation
u_klassencode is highly correlated with S_spracheHigh correlation
S_AB3_HTA is highly correlated with u_fahrausweisHigh correlation
R_anschluss is highly correlated with wime_puenktlich and 1 other fieldsHigh correlation
R_stoerung is highly correlated with wime_puenktlich and 1 other fieldsHigh correlation
device_type is highly correlated with participant_idHigh correlation
dispcode is highly correlated with S_spracheHigh correlation
u_ticket is highly correlated with S_spracheHigh correlation
u_fahrausweis is highly correlated with S_AB3_HTAHigh correlation
R_zweck is highly correlated with S_spracheHigh correlation
ft_tu is highly correlated with ft_startort_uic and 4 other fieldsHigh correlation
ft_vm_kurz is highly correlated with ft_startort_uic and 3 other fieldsHigh correlation
Kommentar has 168258 (73.0%) missing values Missing
wime_personal has 151269 (65.6%) missing values Missing
wime_komfort has 50871 (22.1%) missing values Missing
wime_sauberkeit has 47991 (20.8%) missing values Missing
wime_puenktlich has 47365 (20.6%) missing values Missing
wime_platzangebot has 46570 (20.2%) missing values Missing
wime_gesamtzuf has 39319 (17.1%) missing values Missing
wime_preis_leistung has 15523 (6.7%) missing values Missing
wime_fahrplan has 8358 (3.6%) missing values Missing
wime_oes_fahrt has 55345 (24.0%) missing values Missing
S_alter has 5860 (2.5%) missing values Missing
S_sex has 5562 (2.4%) missing values Missing
S_wohnsitz has 5561 (2.4%) missing values Missing
u_klassencode has 6082 (2.6%) missing values Missing
S_AB3_HTA has 12328 (5.3%) missing values Missing
R_anschluss has 102538 (44.5%) missing values Missing
R_stoerung has 48374 (21.0%) missing values Missing
device_type has 82382 (35.8%) missing values Missing
dispcode has 82382 (35.8%) missing values Missing
u_ticket has 40694 (17.7%) missing values Missing
u_fahrausweis has 110636 (48.0%) missing values Missing
u_preis has 22060 (9.6%) missing values Missing
R_zweck has 5509 (2.4%) missing values Missing
ft_abfahrt has 38826 (16.8%) missing values Missing
ft_ankunft has 38826 (16.8%) missing values Missing
ft_startort_uic has 38826 (16.8%) missing values Missing
ft_tu has 38826 (16.8%) missing values Missing
ft_vm has 38826 (16.8%) missing values Missing
ft_vm_kurz has 38826 (16.8%) missing values Missing
ft_zielort_uic has 38826 (16.8%) missing values Missing
fg_abfahrt has 27962 (12.1%) missing values Missing
fg_ankunft has 27962 (12.1%) missing values Missing
fg_startort_uic has 27962 (12.1%) missing values Missing
fg_zielort_uic has 27962 (12.1%) missing values Missing
fg_startort has 6747 (2.9%) missing values Missing
fg_zielort has 6744 (2.9%) missing values Missing
ft_startort has 17653 (7.7%) missing values Missing
ft_zielort has 17650 (7.7%) missing values Missing
u_preis is highly skewed (γ1 = 20.00160275) Skewed
ft_startort_uic is highly skewed (γ1 = -38.67778891) Skewed
fg_startort_uic is highly skewed (γ1 = -54.04320342) Skewed
fg_zielort_uic is highly skewed (γ1 = -45.87708909) Skewed
Kommentar is uniformly distributed Uniform
participant_id has unique values Unique
wime_komfort has 2958 (1.3%) zeros Zeros
wime_puenktlich has 4706 (2.0%) zeros Zeros
wime_platzangebot has 6408 (2.8%) zeros Zeros
wime_preis_leistung has 6484 (2.8%) zeros Zeros
wime_fahrplan has 5359 (2.3%) zeros Zeros

Reproduction

Analysis started2022-11-25 11:54:17.320043
Analysis finished2022-11-25 12:18:49.550860
Duration24 minutes and 32.23 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

participant_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct230436
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean375292.0741
Minimum642
Maximum589965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-25T13:18:49.672688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum642
5-th percentile66072.5
Q1339252.75
median428705.5
Q3508468.25
95-th percentile574189.25
Maximum589965
Range589323
Interquartile range (IQR)169215.5

Descriptive statistics

Standard deviation172347.8836
Coefficient of variation (CV)0.4592366734
Kurtosis-0.7924523896
Mean375292.0741
Median Absolute Deviation (MAD)84682.5
Skewness-0.7931829375
Sum8.648080438 × 1010
Variance2.9703793 × 1010
MonotonicityStrictly increasing
2022-11-25T13:18:49.802221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6421
 
< 0.1%
4811851
 
< 0.1%
4812281
 
< 0.1%
4812291
 
< 0.1%
4812301
 
< 0.1%
4812311
 
< 0.1%
4812321
 
< 0.1%
4812341
 
< 0.1%
4812351
 
< 0.1%
4812361
 
< 0.1%
Other values (230426)230426
> 99.9%
ValueCountFrequency (%)
6421
< 0.1%
6571
< 0.1%
247561
< 0.1%
256201
< 0.1%
412151
< 0.1%
413051
< 0.1%
413341
< 0.1%
413761
< 0.1%
414231
< 0.1%
414591
< 0.1%
ValueCountFrequency (%)
5899651
< 0.1%
5899621
< 0.1%
5899591
< 0.1%
5899571
< 0.1%
5899561
< 0.1%
5899551
< 0.1%
5899541
< 0.1%
5899511
< 0.1%
5899461
< 0.1%
5899451
< 0.1%

u_date
Date

Distinct1330
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Minimum2019-01-01 00:00:00
Maximum2022-11-16 00:00:00
2022-11-25T13:18:49.923510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:18:50.054558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Kommentar
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct60552
Distinct (%)97.4%
Missing168258
Missing (%)73.0%
Memory size1.8 MiB
Nein
 
148
-
 
98
 
81
Keine
 
78
nein
 
68
Other values (60547)
61705 

Length

Max length2058
Median length1360
Mean length173.7346167
Min length0

Characters and Unicode

Total characters10802471
Distinct characters146
Distinct categories16 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60158 ?
Unique (%)96.8%

Sample

1st rowHabe schon mehrmals erlebt, dass es im Speisewagen keine Gipfeli gab am Morgen. Das war jeweils ärgerlich.
2nd rowAnsteben, dass auch in gut frequentierte periphere Zentren eine Fahrplanverdichtung erfolgt.Die Trennung von Fernverkehr und Regionalverkehr ist in Frage zu stellen. Schade, dass ich zu meiner ersten Fahrt am Tag Stellung nehmen muss, beginne ich dich mein Reiseprogramm aus Platzgründen im Zug bewusst früher als es nötig wäre...
3rd rowDie 1. Klasse muss deutluch aufgewertet werden. Die neuen untergeordneten Haltestellen wie Zürich Altstetten und Zürich Oerlikon trüben Fahrerlebnis massiv.
4th rowBessere (neue!) Züge in der Westschweiz!!! Längere Züge zwischen Bern und Genève (vor allem zwischen 16 Uhr und 19 Uhr) (mindestens +3 Wagen). Mehr 1. Klasse Gutscheine für 2.Klass-GA-Inhaber.
5th row- mehr Monitore - die Monitore so platzieren, dass man sie nicht suchen muss, wenn man knapp zum Bahnhof kommt, vor allem jetzt, wo alle Züge ständig auf anderen Gleisen fahren und die Wege sowieso

Common Values

ValueCountFrequency (%)
Nein148
 
0.1%
-98
 
< 0.1%
81
 
< 0.1%
Keine78
 
< 0.1%
nein68
 
< 0.1%
Mehr Sitzplätze40
 
< 0.1%
Pünktlichkeit36
 
< 0.1%
.35
 
< 0.1%
keine33
 
< 0.1%
Preise senken32
 
< 0.1%
Other values (60542)61529
 
26.7%
(Missing)168258
73.0%

Length

2022-11-25T13:18:50.203104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
die43612
 
2.6%
der36315
 
2.2%
ich35132
 
2.1%
und34800
 
2.1%
in26761
 
1.6%
nicht24846
 
1.5%
ist20547
 
1.2%
es20325
 
1.2%
das18663
 
1.1%
zu18527
 
1.1%
Other values (71786)1389423
83.3%

Most occurring characters

ValueCountFrequency (%)
1649434
15.3%
e1327903
 
12.3%
n847509
 
7.8%
i658245
 
6.1%
r579122
 
5.4%
s525320
 
4.9%
t516616
 
4.8%
a484606
 
4.5%
h413879
 
3.8%
d324527
 
3.0%
Other values (136)3475310
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8206781
76.0%
Space Separator1649458
 
15.3%
Uppercase Letter585779
 
5.4%
Other Punctuation249385
 
2.3%
Decimal Number59957
 
0.6%
Dash Punctuation24143
 
0.2%
Open Punctuation12244
 
0.1%
Close Punctuation12118
 
0.1%
Math Symbol1384
 
< 0.1%
Initial Punctuation924
 
< 0.1%
Other values (6)298
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1327903
16.2%
n847509
 
10.3%
i658245
 
8.0%
r579122
 
7.1%
s525320
 
6.4%
t516616
 
6.3%
a484606
 
5.9%
h413879
 
5.0%
d324527
 
4.0%
u322966
 
3.9%
Other values (40)2206088
26.9%
Uppercase Letter
ValueCountFrequency (%)
B63969
 
10.9%
S60102
 
10.3%
Z47613
 
8.1%
A45854
 
7.8%
D36245
 
6.2%
W26608
 
4.5%
M26608
 
4.5%
I25603
 
4.4%
P25343
 
4.3%
F24028
 
4.1%
Other values (24)203806
34.8%
Other Punctuation
ValueCountFrequency (%)
.137322
55.1%
,69948
28.0%
!20445
 
8.2%
?6965
 
2.8%
:6735
 
2.7%
/4404
 
1.8%
;1112
 
0.4%
'938
 
0.4%
593
 
0.2%
&368
 
0.1%
Other values (6)555
 
0.2%
Decimal Number
ValueCountFrequency (%)
114409
24.0%
211834
19.7%
010771
18.0%
35443
 
9.1%
54437
 
7.4%
43605
 
6.0%
72584
 
4.3%
62477
 
4.1%
92201
 
3.7%
82196
 
3.7%
Math Symbol
ValueCountFrequency (%)
>632
45.7%
+271
19.6%
=256
18.5%
<207
 
15.0%
×8
 
0.6%
~7
 
0.5%
|3
 
0.2%
Open Punctuation
ValueCountFrequency (%)
(11528
94.2%
641
 
5.2%
67
 
0.5%
[7
 
0.1%
{1
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
`36
63.2%
¨11
 
19.3%
´6
 
10.5%
^4
 
7.0%
Dash Punctuation
ValueCountFrequency (%)
-23941
99.2%
108
 
0.4%
94
 
0.4%
Close Punctuation
ValueCountFrequency (%)
)12106
99.9%
]8
 
0.1%
}4
 
< 0.1%
Initial Punctuation
ValueCountFrequency (%)
639
69.2%
233
 
25.2%
«52
 
5.6%
Final Punctuation
ValueCountFrequency (%)
48
36.4%
»48
36.4%
36
27.3%
Space Separator
ValueCountFrequency (%)
1649434
> 99.9%
 24
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
10
90.9%
$1
 
9.1%
Other Number
ValueCountFrequency (%)
½7
87.5%
¼1
 
12.5%
Other Symbol
ValueCountFrequency (%)
°54
100.0%
Connector Punctuation
ValueCountFrequency (%)
_36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8792560
81.4%
Common2009911
 
18.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1327903
15.1%
n847509
 
9.6%
i658245
 
7.5%
r579122
 
6.6%
s525320
 
6.0%
t516616
 
5.9%
a484606
 
5.5%
h413879
 
4.7%
d324527
 
3.7%
u322966
 
3.7%
Other values (74)2791867
31.8%
Common
ValueCountFrequency (%)
1649434
82.1%
.137322
 
6.8%
,69948
 
3.5%
-23941
 
1.2%
!20445
 
1.0%
114409
 
0.7%
)12106
 
0.6%
211834
 
0.6%
(11528
 
0.6%
010771
 
0.5%
Other values (52)48173
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII10633036
98.4%
None166959
 
1.5%
Punctuation2466
 
< 0.1%
Currency Symbols10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1649434
15.5%
e1327903
12.5%
n847509
 
8.0%
i658245
 
6.2%
r579122
 
5.4%
s525320
 
4.9%
t516616
 
4.9%
a484606
 
4.6%
h413879
 
3.9%
d324527
 
3.1%
Other values (83)3305875
31.1%
None
ValueCountFrequency (%)
ü80947
48.5%
ä55722
33.4%
ö26423
 
15.8%
Ö1290
 
0.8%
Ü824
 
0.5%
ß646
 
0.4%
Ä484
 
0.3%
é172
 
0.1%
è57
 
< 0.1%
°54
 
< 0.1%
Other values (32)340
 
0.2%
Punctuation
ValueCountFrequency (%)
641
26.0%
639
25.9%
593
24.0%
233
 
9.4%
108
 
4.4%
94
 
3.8%
67
 
2.7%
48
 
1.9%
36
 
1.5%
7
 
0.3%
Currency Symbols
ValueCountFrequency (%)
10
100.0%

wime_personal
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct13
Distinct (%)< 0.1%
Missing151269
Missing (%)65.6%
Infinite0
Infinite (%)0.0%
Mean89.89734078
Minimum0
Maximum100
Zeros701
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-25T13:18:50.318755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q177.77777778
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)22.22222222

Descriptive statistics

Standard deviation17.81349174
Coefficient of variation (CV)0.1981537116
Kurtosis6.733489346
Mean89.89734078
Median Absolute Deviation (MAD)0
Skewness-2.362104001
Sum7116902.778
Variance317.320488
MonotonicityNot monotonic
2022-11-25T13:18:50.428093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10052039
 
22.6%
759762
 
4.2%
88.888888895378
 
2.3%
77.777777784851
 
2.1%
66.666666671880
 
0.8%
501777
 
0.8%
44.44444444886
 
0.4%
55.55555556817
 
0.4%
0701
 
0.3%
25445
 
0.2%
Other values (3)631
 
0.3%
(Missing)151269
65.6%
ValueCountFrequency (%)
0701
 
0.3%
11.11111111138
 
0.1%
22.22222222228
 
0.1%
25445
 
0.2%
33.33333333265
 
0.1%
44.44444444886
 
0.4%
501777
 
0.8%
55.55555556817
 
0.4%
66.666666671880
 
0.8%
759762
4.2%
ValueCountFrequency (%)
10052039
22.6%
88.888888895378
 
2.3%
77.777777784851
 
2.1%
759762
 
4.2%
66.666666671880
 
0.8%
55.55555556817
 
0.4%
501777
 
0.8%
44.44444444886
 
0.4%
33.33333333265
 
0.1%
25445
 
0.2%

wime_komfort
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct13
Distinct (%)< 0.1%
Missing50871
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean78.94156248
Minimum0
Maximum100
Zeros2958
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-25T13:18:50.521524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.33333333
Q175
median77.77777778
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation22.80893395
Coefficient of variation (CV)0.2889344121
Kurtosis1.479833592
Mean78.94156248
Median Absolute Deviation (MAD)22.22222222
Skewness-1.241166618
Sum14175141.67
Variance520.2474678
MonotonicityNot monotonic
2022-11-25T13:18:50.647228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10068640
29.8%
7537779
16.4%
77.7777777817476
 
7.6%
88.8888888912656
 
5.5%
66.6666666710829
 
4.7%
5010475
 
4.5%
55.555555566008
 
2.6%
44.444444444853
 
2.1%
02958
 
1.3%
252840
 
1.2%
Other values (3)5051
 
2.2%
(Missing)50871
22.1%
ValueCountFrequency (%)
02958
 
1.3%
11.111111111003
 
0.4%
22.222222221686
 
0.7%
252840
 
1.2%
33.333333332362
 
1.0%
44.444444444853
 
2.1%
5010475
 
4.5%
55.555555566008
 
2.6%
66.6666666710829
 
4.7%
7537779
16.4%
ValueCountFrequency (%)
10068640
29.8%
88.8888888912656
 
5.5%
77.7777777817476
 
7.6%
7537779
16.4%
66.6666666710829
 
4.7%
55.555555566008
 
2.6%
5010475
 
4.5%
44.444444444853
 
2.1%
33.333333332362
 
1.0%
252840
 
1.2%

wime_sauberkeit
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct13
Distinct (%)< 0.1%
Missing47991
Missing (%)20.8%
Infinite0
Infinite (%)0.0%
Mean79.26533415
Minimum0
Maximum100
Zeros1628
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-25T13:18:50.740044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44.44444444
Q175
median77.77777778
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation21.46045723
Coefficient of variation (CV)0.2707420268
Kurtosis1.140701001
Mean79.26533415
Median Absolute Deviation (MAD)22.22222222
Skewness-1.100859622
Sum14461563.89
Variance460.5512243
MonotonicityNot monotonic
2022-11-25T13:18:50.849908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10067360
29.2%
7540500
17.6%
77.7777777818048
 
7.8%
88.8888888913669
 
5.9%
5012953
 
5.6%
66.6666666710791
 
4.7%
55.555555565597
 
2.4%
44.444444444517
 
2.0%
253281
 
1.4%
33.333333332172
 
0.9%
Other values (3)3557
 
1.5%
(Missing)47991
20.8%
ValueCountFrequency (%)
01628
 
0.7%
11.11111111606
 
0.3%
22.222222221323
 
0.6%
253281
 
1.4%
33.333333332172
 
0.9%
44.444444444517
 
2.0%
5012953
 
5.6%
55.555555565597
 
2.4%
66.6666666710791
 
4.7%
7540500
17.6%
ValueCountFrequency (%)
10067360
29.2%
88.8888888913669
 
5.9%
77.7777777818048
 
7.8%
7540500
17.6%
66.6666666710791
 
4.7%
55.555555565597
 
2.4%
5012953
 
5.6%
44.444444444517
 
2.0%
33.333333332172
 
0.9%
253281
 
1.4%

wime_puenktlich
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct13
Distinct (%)< 0.1%
Missing47365
Missing (%)20.6%
Infinite0
Infinite (%)0.0%
Mean88.88567216
Minimum0
Maximum100
Zeros4706
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-25T13:18:50.991801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.33333333
Q188.88888889
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)11.11111111

Descriptive statistics

Standard deviation22.15786224
Coefficient of variation (CV)0.2492849713
Kurtosis6.132215583
Mean88.88567216
Median Absolute Deviation (MAD)0
Skewness-2.504766294
Sum16272388.89
Variance490.9708589
MonotonicityNot monotonic
2022-11-25T13:18:51.099085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100126462
54.9%
7517816
 
7.7%
88.8888888911720
 
5.1%
77.777777787334
 
3.2%
04706
 
2.0%
504238
 
1.8%
66.666666672961
 
1.3%
252168
 
0.9%
44.444444441568
 
0.7%
55.555555561485
 
0.6%
Other values (3)2613
 
1.1%
(Missing)47365
 
20.6%
ValueCountFrequency (%)
04706
 
2.0%
11.11111111630
 
0.3%
22.22222222990
 
0.4%
252168
 
0.9%
33.33333333993
 
0.4%
44.444444441568
 
0.7%
504238
 
1.8%
55.555555561485
 
0.6%
66.666666672961
 
1.3%
7517816
7.7%
ValueCountFrequency (%)
100126462
54.9%
88.8888888911720
 
5.1%
77.777777787334
 
3.2%
7517816
 
7.7%
66.666666672961
 
1.3%
55.555555561485
 
0.6%
504238
 
1.8%
44.444444441568
 
0.7%
33.33333333993
 
0.4%
252168
 
0.9%

wime_platzangebot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct13
Distinct (%)< 0.1%
Missing46570
Missing (%)20.2%
Infinite0
Infinite (%)0.0%
Mean80.19040436
Minimum0
Maximum100
Zeros6408
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-25T13:18:51.198371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.22222222
Q175
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation26.76994683
Coefficient of variation (CV)0.3338298023
Kurtosis1.366630743
Mean80.19040436
Median Absolute Deviation (MAD)0
Skewness-1.448636276
Sum14744288.89
Variance716.6300534
MonotonicityNot monotonic
2022-11-25T13:18:51.297995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10092774
40.3%
7526389
 
11.5%
77.7777777812108
 
5.3%
88.8888888910362
 
4.5%
509925
 
4.3%
66.666666676883
 
3.0%
06408
 
2.8%
254651
 
2.0%
55.555555564103
 
1.8%
44.444444443944
 
1.7%
Other values (3)6319
 
2.7%
(Missing)46570
20.2%
ValueCountFrequency (%)
06408
 
2.8%
11.111111111554
 
0.7%
22.222222222323
 
1.0%
254651
 
2.0%
33.333333332442
 
1.1%
44.444444443944
 
1.7%
509925
 
4.3%
55.555555564103
 
1.8%
66.666666676883
 
3.0%
7526389
11.5%
ValueCountFrequency (%)
10092774
40.3%
88.8888888910362
 
4.5%
77.7777777812108
 
5.3%
7526389
 
11.5%
66.666666676883
 
3.0%
55.555555564103
 
1.8%
509925
 
4.3%
44.444444443944
 
1.7%
33.333333332442
 
1.1%
254651
 
2.0%

wime_gesamtzuf
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct13
Distinct (%)< 0.1%
Missing39319
Missing (%)17.1%
Infinite0
Infinite (%)0.0%
Mean84.57496659
Minimum0
Maximum100
Zeros2071
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-25T13:18:51.389619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q175
median88.88888889
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation19.58447459
Coefficient of variation (CV)0.2315634919
Kurtosis3.735611901
Mean84.57496659
Median Absolute Deviation (MAD)11.11111111
Skewness-1.722032645
Sum16163713.89
Variance383.5516449
MonotonicityNot monotonic
2022-11-25T13:18:51.484848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10089357
38.8%
7539018
16.9%
88.8888888920408
 
8.9%
77.7777777816835
 
7.3%
507273
 
3.2%
66.666666676781
 
2.9%
55.555555562808
 
1.2%
44.444444442200
 
1.0%
02071
 
0.9%
251925
 
0.8%
Other values (3)2441
 
1.1%
(Missing)39319
17.1%
ValueCountFrequency (%)
02071
 
0.9%
11.11111111476
 
0.2%
22.22222222916
 
0.4%
251925
 
0.8%
33.333333331049
 
0.5%
44.444444442200
 
1.0%
507273
 
3.2%
55.555555562808
 
1.2%
66.666666676781
 
2.9%
7539018
16.9%
ValueCountFrequency (%)
10089357
38.8%
88.8888888920408
 
8.9%
77.7777777816835
 
7.3%
7539018
16.9%
66.666666676781
 
2.9%
55.555555562808
 
1.2%
507273
 
3.2%
44.444444442200
 
1.0%
33.333333331049
 
0.5%
251925
 
0.8%

wime_preis_leistung
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct13
Distinct (%)< 0.1%
Missing15523
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean73.86437251
Minimum0
Maximum100
Zeros6484
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-25T13:18:51.571656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q150
median75
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)50

Descriptive statistics

Standard deviation26.4163289
Coefficient of variation (CV)0.3576328886
Kurtosis0.2277005886
Mean73.86437251
Median Absolute Deviation (MAD)25
Skewness-0.9184731838
Sum15874413.89
Variance697.8224327
MonotonicityNot monotonic
2022-11-25T13:18:51.679354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10076703
33.3%
7546435
20.2%
5026008
 
11.3%
77.7777777813319
 
5.8%
66.6666666710095
 
4.4%
258592
 
3.7%
88.888888897822
 
3.4%
06484
 
2.8%
55.555555566301
 
2.7%
44.444444446265
 
2.7%
Other values (3)6889
 
3.0%
(Missing)15523
 
6.7%
ValueCountFrequency (%)
06484
 
2.8%
11.111111111224
 
0.5%
22.222222222597
 
1.1%
258592
 
3.7%
33.333333333068
 
1.3%
44.444444446265
 
2.7%
5026008
11.3%
55.555555566301
 
2.7%
66.6666666710095
 
4.4%
7546435
20.2%
ValueCountFrequency (%)
10076703
33.3%
88.888888897822
 
3.4%
77.7777777813319
 
5.8%
7546435
20.2%
66.6666666710095
 
4.4%
55.555555566301
 
2.7%
5026008
 
11.3%
44.444444446265
 
2.7%
33.333333333068
 
1.3%
258592
 
3.7%

wime_fahrplan
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct13
Distinct (%)< 0.1%
Missing8358
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean83.40204042
Minimum0
Maximum100
Zeros5359
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-25T13:18:51.785390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q175
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation23.9071193
Coefficient of variation (CV)0.2866490937
Kurtosis2.541959888
Mean83.40204042
Median Absolute Deviation (MAD)0
Skewness-1.688037225
Sum18521758.33
Variance571.5503531
MonotonicityNot monotonic
2022-11-25T13:18:51.882640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100120650
52.4%
7533552
 
14.6%
77.7777777814716
 
6.4%
88.8888888912691
 
5.5%
5010888
 
4.7%
66.666666677761
 
3.4%
05359
 
2.3%
254465
 
1.9%
55.555555563971
 
1.7%
44.444444443752
 
1.6%
Other values (3)4273
 
1.9%
(Missing)8358
 
3.6%
ValueCountFrequency (%)
05359
 
2.3%
11.11111111843
 
0.4%
22.222222221512
 
0.7%
254465
 
1.9%
33.333333331918
 
0.8%
44.444444443752
 
1.6%
5010888
 
4.7%
55.555555563971
 
1.7%
66.666666677761
 
3.4%
7533552
14.6%
ValueCountFrequency (%)
100120650
52.4%
88.8888888912691
 
5.5%
77.7777777814716
 
6.4%
7533552
 
14.6%
66.666666677761
 
3.4%
55.555555563971
 
1.7%
5010888
 
4.7%
44.444444443752
 
1.6%
33.333333331918
 
0.8%
254465
 
1.9%

wime_oes_fahrt
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct13
Distinct (%)< 0.1%
Missing55345
Missing (%)24.0%
Infinite0
Infinite (%)0.0%
Mean90.69958225
Minimum0
Maximum100
Zeros410
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-25T13:18:51.984537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile66.66666667
Q177.77777778
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)22.22222222

Descriptive statistics

Standard deviation14.73856196
Coefficient of variation (CV)0.1624986753
Kurtosis5.585256905
Mean90.69958225
Median Absolute Deviation (MAD)0
Skewness-2.013682819
Sum15880680.56
Variance217.2252087
MonotonicityNot monotonic
2022-11-25T13:18:52.082653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100109793
47.6%
7523797
 
10.3%
88.8888888917023
 
7.4%
77.7777777812838
 
5.6%
66.666666674233
 
1.8%
503336
 
1.4%
55.555555561487
 
0.6%
44.44444444946
 
0.4%
25594
 
0.3%
0410
 
0.2%
Other values (3)634
 
0.3%
(Missing)55345
24.0%
ValueCountFrequency (%)
0410
 
0.2%
11.11111111106
 
< 0.1%
22.22222222211
 
0.1%
25594
 
0.3%
33.33333333317
 
0.1%
44.44444444946
 
0.4%
503336
 
1.4%
55.555555561487
 
0.6%
66.666666674233
 
1.8%
7523797
10.3%
ValueCountFrequency (%)
100109793
47.6%
88.8888888917023
 
7.4%
77.7777777812838
 
5.6%
7523797
 
10.3%
66.666666674233
 
1.8%
55.555555561487
 
0.6%
503336
 
1.4%
44.44444444946
 
0.4%
33.33333333317
 
0.1%
25594
 
0.3%

S_sprache
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size225.3 KiB
Deutsch
230436 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1613052
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeutsch
2nd rowDeutsch
3rd rowDeutsch
4th rowDeutsch
5th rowDeutsch

Common Values

ValueCountFrequency (%)
Deutsch230436
100.0%

Length

2022-11-25T13:18:52.176201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-25T13:18:52.316742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
deutsch230436
100.0%

Most occurring characters

ValueCountFrequency (%)
D230436
14.3%
e230436
14.3%
u230436
14.3%
t230436
14.3%
s230436
14.3%
c230436
14.3%
h230436
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1382616
85.7%
Uppercase Letter230436
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e230436
16.7%
u230436
16.7%
t230436
16.7%
s230436
16.7%
c230436
16.7%
h230436
16.7%
Uppercase Letter
ValueCountFrequency (%)
D230436
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1613052
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D230436
14.3%
e230436
14.3%
u230436
14.3%
t230436
14.3%
s230436
14.3%
c230436
14.3%
h230436
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1613052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D230436
14.3%
e230436
14.3%
u230436
14.3%
t230436
14.3%
s230436
14.3%
c230436
14.3%
h230436
14.3%

S_alter
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct89
Distinct (%)< 0.1%
Missing5860
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean51.63611873
Minimum10
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-25T13:18:52.413476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile23
Q140
median53
Q364
95-th percentile75
Maximum98
Range88
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.93706606
Coefficient of variation (CV)0.3086418276
Kurtosis-0.6199927104
Mean51.63611873
Median Absolute Deviation (MAD)12
Skewness-0.3076231175
Sum11596233
Variance253.9900745
MonotonicityNot monotonic
2022-11-25T13:18:52.545674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
556334
 
2.7%
606155
 
2.7%
505969
 
2.6%
585521
 
2.4%
565513
 
2.4%
575504
 
2.4%
525439
 
2.4%
655402
 
2.3%
545245
 
2.3%
535205
 
2.3%
Other values (79)168289
73.0%
(Missing)5860
 
2.5%
ValueCountFrequency (%)
1075
 
< 0.1%
1142
 
< 0.1%
1248
 
< 0.1%
1369
 
< 0.1%
14224
 
0.1%
15562
 
0.2%
161205
0.5%
171498
0.7%
181639
0.7%
191431
0.6%
ValueCountFrequency (%)
985
 
< 0.1%
972
 
< 0.1%
962
 
< 0.1%
959
 
< 0.1%
9410
 
< 0.1%
937
 
< 0.1%
928
 
< 0.1%
9126
< 0.1%
9064
< 0.1%
8954
< 0.1%

S_sex
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing5562
Missing (%)2.4%
Memory size225.3 KiB
weiblich
123941 
männlich
100264 
divers
 
669

Length

Max length8
Median length8
Mean length7.994050001
Min length6

Characters and Unicode

Total characters1797654
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmännlich
2nd rowmännlich
3rd rowweiblich
4th rowweiblich
5th rowweiblich

Common Values

ValueCountFrequency (%)
weiblich123941
53.8%
männlich100264
43.5%
divers669
 
0.3%
(Missing)5562
 
2.4%

Length

2022-11-25T13:18:52.668163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-25T13:18:52.796515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
weiblich123941
55.1%
männlich100264
44.6%
divers669
 
0.3%

Most occurring characters

ValueCountFrequency (%)
i348815
19.4%
l224205
12.5%
c224205
12.5%
h224205
12.5%
n200528
11.2%
e124610
 
6.9%
w123941
 
6.9%
b123941
 
6.9%
m100264
 
5.6%
ä100264
 
5.6%
Other values (4)2676
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1797654
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i348815
19.4%
l224205
12.5%
c224205
12.5%
h224205
12.5%
n200528
11.2%
e124610
 
6.9%
w123941
 
6.9%
b123941
 
6.9%
m100264
 
5.6%
ä100264
 
5.6%
Other values (4)2676
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1797654
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i348815
19.4%
l224205
12.5%
c224205
12.5%
h224205
12.5%
n200528
11.2%
e124610
 
6.9%
w123941
 
6.9%
b123941
 
6.9%
m100264
 
5.6%
ä100264
 
5.6%
Other values (4)2676
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1697390
94.4%
None100264
 
5.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i348815
20.6%
l224205
13.2%
c224205
13.2%
h224205
13.2%
n200528
11.8%
e124610
 
7.3%
w123941
 
7.3%
b123941
 
7.3%
m100264
 
5.9%
d669
 
< 0.1%
Other values (3)2007
 
0.1%
None
ValueCountFrequency (%)
ä100264
100.0%

S_wohnsitz
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing5561
Missing (%)2.4%
Memory size225.3 KiB
In der Schweiz / Liechtenstein
219516 
In einem anderen Land
 
5359

Length

Max length30
Median length30
Mean length29.78552084
Min length21

Characters and Unicode

Total characters6698019
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIn der Schweiz / Liechtenstein
2nd rowIn der Schweiz / Liechtenstein
3rd rowIn der Schweiz / Liechtenstein
4th rowIn der Schweiz / Liechtenstein
5th rowIn der Schweiz / Liechtenstein

Common Values

ValueCountFrequency (%)
In der Schweiz / Liechtenstein219516
95.3%
In einem anderen Land5359
 
2.3%
(Missing)5561
 
2.4%

Length

2022-11-25T13:18:52.900601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-25T13:18:52.999857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
in224875
20.1%
der219516
19.6%
schweiz219516
19.6%
219516
19.6%
liechtenstein219516
19.6%
einem5359
 
0.5%
anderen5359
 
0.5%
land5359
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e1119016
16.7%
894141
13.3%
n685343
10.2%
i663907
9.9%
c439032
 
6.6%
h439032
 
6.6%
t439032
 
6.6%
d230234
 
3.4%
I224875
 
3.4%
L224875
 
3.4%
Other values (8)1338532
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4915096
73.4%
Space Separator894141
 
13.3%
Uppercase Letter669266
 
10.0%
Other Punctuation219516
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1119016
22.8%
n685343
13.9%
i663907
13.5%
c439032
 
8.9%
h439032
 
8.9%
t439032
 
8.9%
d230234
 
4.7%
r224875
 
4.6%
s219516
 
4.5%
w219516
 
4.5%
Other values (3)235593
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
I224875
33.6%
L224875
33.6%
S219516
32.8%
Space Separator
ValueCountFrequency (%)
894141
100.0%
Other Punctuation
ValueCountFrequency (%)
/219516
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5584362
83.4%
Common1113657
 
16.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1119016
20.0%
n685343
12.3%
i663907
11.9%
c439032
 
7.9%
h439032
 
7.9%
t439032
 
7.9%
d230234
 
4.1%
I224875
 
4.0%
L224875
 
4.0%
r224875
 
4.0%
Other values (6)894141
16.0%
Common
ValueCountFrequency (%)
894141
80.3%
/219516
 
19.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII6698019
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1119016
16.7%
894141
13.3%
n685343
10.2%
i663907
9.9%
c439032
 
6.6%
h439032
 
6.6%
t439032
 
6.6%
d230234
 
3.4%
I224875
 
3.4%
L224875
 
3.4%
Other values (8)1338532
20.0%

u_klassencode
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing6082
Missing (%)2.6%
Memory size225.3 KiB
2. Klasse
195783 
1. Klasse
28571 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters2019186
Distinct characters9
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2. Klasse
2nd row1. Klasse
3rd row2. Klasse
4th row2. Klasse
5th row2. Klasse

Common Values

ValueCountFrequency (%)
2. Klasse195783
85.0%
1. Klasse28571
 
12.4%
(Missing)6082
 
2.6%

Length

2022-11-25T13:18:53.095437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-25T13:18:53.212323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
klasse224354
50.0%
2195783
43.6%
128571
 
6.4%

Most occurring characters

ValueCountFrequency (%)
s448708
22.2%
.224354
11.1%
224354
11.1%
K224354
11.1%
l224354
11.1%
a224354
11.1%
e224354
11.1%
2195783
9.7%
128571
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1121770
55.6%
Other Punctuation224354
 
11.1%
Space Separator224354
 
11.1%
Uppercase Letter224354
 
11.1%
Decimal Number224354
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s448708
40.0%
l224354
20.0%
a224354
20.0%
e224354
20.0%
Decimal Number
ValueCountFrequency (%)
2195783
87.3%
128571
 
12.7%
Other Punctuation
ValueCountFrequency (%)
.224354
100.0%
Space Separator
ValueCountFrequency (%)
224354
100.0%
Uppercase Letter
ValueCountFrequency (%)
K224354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1346124
66.7%
Common673062
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
s448708
33.3%
K224354
16.7%
l224354
16.7%
a224354
16.7%
e224354
16.7%
Common
ValueCountFrequency (%)
.224354
33.3%
224354
33.3%
2195783
29.1%
128571
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2019186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s448708
22.2%
.224354
11.1%
224354
11.1%
K224354
11.1%
l224354
11.1%
a224354
11.1%
e224354
11.1%
2195783
9.7%
128571
 
1.4%

S_AB3_HTA
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing12328
Missing (%)5.3%
Memory size225.3 KiB
ja
180314 
nein
37794 

Length

Max length4
Median length2
Mean length2.346562254
Min length2

Characters and Unicode

Total characters511804
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowja
2nd rowja
3rd rowja
4th rowja
5th rowja

Common Values

ValueCountFrequency (%)
ja180314
78.2%
nein37794
 
16.4%
(Missing)12328
 
5.3%

Length

2022-11-25T13:18:53.318023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-25T13:18:53.455788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
ja180314
82.7%
nein37794
 
17.3%

Most occurring characters

ValueCountFrequency (%)
j180314
35.2%
a180314
35.2%
n75588
14.8%
e37794
 
7.4%
i37794
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter511804
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
j180314
35.2%
a180314
35.2%
n75588
14.8%
e37794
 
7.4%
i37794
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Latin511804
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
j180314
35.2%
a180314
35.2%
n75588
14.8%
e37794
 
7.4%
i37794
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII511804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
j180314
35.2%
a180314
35.2%
n75588
14.8%
e37794
 
7.4%
i37794
 
7.4%

R_anschluss
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing102538
Missing (%)44.5%
Memory size225.3 KiB
Ja
121855 
Nein
 
6043

Length

Max length4
Median length2
Mean length2.094497177
Min length2

Characters and Unicode

Total characters267882
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJa
2nd rowJa
3rd rowJa
4th rowJa
5th rowJa

Common Values

ValueCountFrequency (%)
Ja121855
52.9%
Nein6043
 
2.6%
(Missing)102538
44.5%

Length

2022-11-25T13:18:53.568747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-25T13:18:53.673071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
ja121855
95.3%
nein6043
 
4.7%

Most occurring characters

ValueCountFrequency (%)
J121855
45.5%
a121855
45.5%
N6043
 
2.3%
e6043
 
2.3%
i6043
 
2.3%
n6043
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter139984
52.3%
Uppercase Letter127898
47.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a121855
87.0%
e6043
 
4.3%
i6043
 
4.3%
n6043
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
J121855
95.3%
N6043
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin267882
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
J121855
45.5%
a121855
45.5%
N6043
 
2.3%
e6043
 
2.3%
i6043
 
2.3%
n6043
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII267882
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
J121855
45.5%
a121855
45.5%
N6043
 
2.3%
e6043
 
2.3%
i6043
 
2.3%
n6043
 
2.3%

R_stoerung
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing48374
Missing (%)21.0%
Memory size225.3 KiB
Nein
167975 
Ja
 
14087

Length

Max length4
Median length4
Mean length3.845250519
Min length2

Characters and Unicode

Total characters700074
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNein
2nd rowNein
3rd rowNein
4th rowNein
5th rowNein

Common Values

ValueCountFrequency (%)
Nein167975
72.9%
Ja14087
 
6.1%
(Missing)48374
 
21.0%

Length

2022-11-25T13:18:53.771055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-25T13:18:53.894532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
nein167975
92.3%
ja14087
 
7.7%

Most occurring characters

ValueCountFrequency (%)
N167975
24.0%
e167975
24.0%
i167975
24.0%
n167975
24.0%
J14087
 
2.0%
a14087
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter518012
74.0%
Uppercase Letter182062
 
26.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e167975
32.4%
i167975
32.4%
n167975
32.4%
a14087
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
N167975
92.3%
J14087
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin700074
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N167975
24.0%
e167975
24.0%
i167975
24.0%
n167975
24.0%
J14087
 
2.0%
a14087
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII700074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N167975
24.0%
e167975
24.0%
i167975
24.0%
n167975
24.0%
J14087
 
2.0%
a14087
 
2.0%

device_type
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing82382
Missing (%)35.8%
Memory size225.3 KiB
Desktop
99793 
Smartphone
48261 

Length

Max length10
Median length7
Mean length7.97790671
Min length7

Characters and Unicode

Total characters1181161
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDesktop
2nd rowDesktop
3rd rowDesktop
4th rowDesktop
5th rowDesktop

Common Values

ValueCountFrequency (%)
Desktop99793
43.3%
Smartphone48261
20.9%
(Missing)82382
35.8%

Length

2022-11-25T13:18:53.998407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-25T13:18:54.117953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
desktop99793
67.4%
smartphone48261
32.6%

Most occurring characters

ValueCountFrequency (%)
e148054
12.5%
t148054
12.5%
o148054
12.5%
p148054
12.5%
D99793
8.4%
s99793
8.4%
k99793
8.4%
S48261
 
4.1%
m48261
 
4.1%
a48261
 
4.1%
Other values (3)144783
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1033107
87.5%
Uppercase Letter148054
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e148054
14.3%
t148054
14.3%
o148054
14.3%
p148054
14.3%
s99793
9.7%
k99793
9.7%
m48261
 
4.7%
a48261
 
4.7%
r48261
 
4.7%
h48261
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
D99793
67.4%
S48261
32.6%

Most occurring scripts

ValueCountFrequency (%)
Latin1181161
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e148054
12.5%
t148054
12.5%
o148054
12.5%
p148054
12.5%
D99793
8.4%
s99793
8.4%
k99793
8.4%
S48261
 
4.1%
m48261
 
4.1%
a48261
 
4.1%
Other values (3)144783
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1181161
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e148054
12.5%
t148054
12.5%
o148054
12.5%
p148054
12.5%
D99793
8.4%
s99793
8.4%
k99793
8.4%
S48261
 
4.1%
m48261
 
4.1%
a48261
 
4.1%
Other values (3)144783
12.3%

dispcode
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing82382
Missing (%)35.8%
Memory size225.3 KiB
Beendet
113620 
Ausgescreent
31459 
Beendet nach Unterbrechung
 
2975

Length

Max length26
Median length7
Mean length8.444202791
Min length7

Characters and Unicode

Total characters1250198
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBeendet
2nd rowAusgescreent
3rd rowBeendet
4th rowBeendet
5th rowBeendet

Common Values

ValueCountFrequency (%)
Beendet113620
49.3%
Ausgescreent31459
 
13.7%
Beendet nach Unterbrechung2975
 
1.3%
(Missing)82382
35.8%

Length

2022-11-25T13:18:54.224877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-25T13:18:54.334317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
beendet116595
75.7%
ausgescreent31459
 
20.4%
nach2975
 
1.9%
unterbrechung2975
 
1.9%

Most occurring characters

ValueCountFrequency (%)
e450112
36.0%
n156979
 
12.6%
t151029
 
12.1%
B116595
 
9.3%
d116595
 
9.3%
s62918
 
5.0%
c37409
 
3.0%
r37409
 
3.0%
u34434
 
2.8%
g34434
 
2.8%
Other values (6)52284
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1093219
87.4%
Uppercase Letter151029
 
12.1%
Space Separator5950
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e450112
41.2%
n156979
 
14.4%
t151029
 
13.8%
d116595
 
10.7%
s62918
 
5.8%
c37409
 
3.4%
r37409
 
3.4%
u34434
 
3.1%
g34434
 
3.1%
h5950
 
0.5%
Other values (2)5950
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
B116595
77.2%
A31459
 
20.8%
U2975
 
2.0%
Space Separator
ValueCountFrequency (%)
5950
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1244248
99.5%
Common5950
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e450112
36.2%
n156979
 
12.6%
t151029
 
12.1%
B116595
 
9.4%
d116595
 
9.4%
s62918
 
5.1%
c37409
 
3.0%
r37409
 
3.0%
u34434
 
2.8%
g34434
 
2.8%
Other values (5)46334
 
3.7%
Common
ValueCountFrequency (%)
5950
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1250198
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e450112
36.0%
n156979
 
12.6%
t151029
 
12.1%
B116595
 
9.3%
d116595
 
9.3%
s62918
 
5.0%
c37409
 
3.0%
r37409
 
3.0%
u34434
 
2.8%
g34434
 
2.8%
Other values (6)52284
 
4.2%

u_ticket
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing40694
Missing (%)17.7%
Memory size225.4 KiB
Mobile-Ticket
157799 
Online-Ticket
25161 
Easy Ride
 
6463
bedienter Vertrieb
 
319

Length

Max length18
Median length13
Mean length12.87215798
Min length9

Characters and Unicode

Total characters2442389
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMobile-Ticket
2nd rowMobile-Ticket
3rd rowMobile-Ticket
4th rowMobile-Ticket
5th rowMobile-Ticket

Common Values

ValueCountFrequency (%)
Mobile-Ticket157799
68.5%
Online-Ticket25161
 
10.9%
Easy Ride6463
 
2.8%
bedienter Vertrieb319
 
0.1%
(Missing)40694
 
17.7%

Length

2022-11-25T13:18:54.423750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-25T13:18:54.537933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
mobile-ticket157799
80.3%
online-ticket25161
 
12.8%
easy6463
 
3.3%
ride6463
 
3.3%
bedienter319
 
0.2%
vertrieb319
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e373978
15.3%
i373021
15.3%
t183598
7.5%
l182960
7.5%
-182960
7.5%
T182960
7.5%
c182960
7.5%
k182960
7.5%
b158437
6.5%
M157799
6.5%
Other values (12)280756
11.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1873482
76.7%
Uppercase Letter379165
 
15.5%
Dash Punctuation182960
 
7.5%
Space Separator6782
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e373978
20.0%
i373021
19.9%
t183598
9.8%
l182960
9.8%
c182960
9.8%
k182960
9.8%
b158437
8.5%
o157799
8.4%
n50641
 
2.7%
d6782
 
0.4%
Other values (4)20346
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
T182960
48.3%
M157799
41.6%
O25161
 
6.6%
E6463
 
1.7%
R6463
 
1.7%
V319
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
-182960
100.0%
Space Separator
ValueCountFrequency (%)
6782
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2252647
92.2%
Common189742
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e373978
16.6%
i373021
16.6%
t183598
8.2%
l182960
8.1%
T182960
8.1%
c182960
8.1%
k182960
8.1%
b158437
7.0%
M157799
7.0%
o157799
7.0%
Other values (10)116175
 
5.2%
Common
ValueCountFrequency (%)
-182960
96.4%
6782
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2442389
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e373978
15.3%
i373021
15.3%
t183598
7.5%
l182960
7.5%
-182960
7.5%
T182960
7.5%
c182960
7.5%
k182960
7.5%
b158437
6.5%
M157799
6.5%
Other values (12)280756
11.5%

u_fahrausweis
Categorical

HIGH CORRELATION
MISSING

Distinct7
Distinct (%)< 0.1%
Missing110636
Missing (%)48.0%
Memory size225.5 KiB
Normales Billett
91847 
GA
15699 
Sparbillett
 
8084
Spartageskarte
 
3416
Tageskarte
 
459
Other values (2)
 
295

Length

Max length18
Median length16
Mean length13.74565109
Min length2

Characters and Unicode

Total characters1646729
Distinct characters30
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormales Billett
2nd rowNormales Billett
3rd rowNormales Billett
4th rowNormales Billett
5th rowNormales Billett

Common Values

ValueCountFrequency (%)
Normales Billett91847
39.9%
GA15699
 
6.8%
Sparbillett8084
 
3.5%
Spartageskarte3416
 
1.5%
Tageskarte459
 
0.2%
Strecken-/Modulabo216
 
0.1%
seven2579
 
< 0.1%
(Missing)110636
48.0%

Length

2022-11-25T13:18:54.650597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-25T13:18:54.775873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normales91847
43.4%
billett91847
43.4%
ga15699
 
7.4%
sparbillett8084
 
3.8%
spartageskarte3416
 
1.6%
tageskarte459
 
0.2%
strecken-/modulabo216
 
0.1%
seven2579
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
l291925
17.7%
t207369
12.6%
e200118
12.2%
a111313
 
6.8%
r107438
 
6.5%
i99931
 
6.1%
s95801
 
5.8%
o92279
 
5.6%
N91847
 
5.6%
m91847
 
5.6%
Other values (20)256861
15.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1326809
80.6%
Uppercase Letter227483
 
13.8%
Space Separator91847
 
5.6%
Dash Punctuation216
 
< 0.1%
Other Punctuation216
 
< 0.1%
Decimal Number158
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l291925
22.0%
t207369
15.6%
e200118
15.1%
a111313
 
8.4%
r107438
 
8.1%
i99931
 
7.5%
s95801
 
7.2%
o92279
 
7.0%
m91847
 
6.9%
p11500
 
0.9%
Other values (8)17288
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
N91847
40.4%
B91847
40.4%
G15699
 
6.9%
A15699
 
6.9%
S11716
 
5.2%
T459
 
0.2%
M216
 
0.1%
Decimal Number
ValueCountFrequency (%)
279
50.0%
579
50.0%
Space Separator
ValueCountFrequency (%)
91847
100.0%
Dash Punctuation
ValueCountFrequency (%)
-216
100.0%
Other Punctuation
ValueCountFrequency (%)
/216
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1554292
94.4%
Common92437
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
l291925
18.8%
t207369
13.3%
e200118
12.9%
a111313
 
7.2%
r107438
 
6.9%
i99931
 
6.4%
s95801
 
6.2%
o92279
 
5.9%
N91847
 
5.9%
m91847
 
5.9%
Other values (15)164424
10.6%
Common
ValueCountFrequency (%)
91847
99.4%
-216
 
0.2%
/216
 
0.2%
279
 
0.1%
579
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1646729
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l291925
17.7%
t207369
12.6%
e200118
12.2%
a111313
 
6.8%
r107438
 
6.5%
i99931
 
6.1%
s95801
 
5.8%
o92279
 
5.6%
N91847
 
5.6%
m91847
 
5.6%
Other values (20)256861
15.6%

u_preis
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct1452
Distinct (%)0.7%
Missing22060
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean33.51653165
Minimum0
Maximum6300
Zeros42
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-25T13:18:54.892500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.8
Q18.2
median16
Q329.1
95-th percentile61
Maximum6300
Range6300
Interquartile range (IQR)20.9

Descriptive statistics

Standard deviation204.0820667
Coefficient of variation (CV)6.088997179
Kurtosis449.324539
Mean33.51653165
Median Absolute Deviation (MAD)9.2
Skewness20.00160275
Sum6984040.8
Variance41649.48994
MonotonicityNot monotonic
2022-11-25T13:18:55.012677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
134883
 
2.1%
6.84779
 
2.1%
3.43955
 
1.7%
8.83022
 
1.3%
6.22826
 
1.2%
282457
 
1.1%
5.82410
 
1.0%
142392
 
1.0%
3.72344
 
1.0%
172337
 
1.0%
Other values (1442)176971
76.8%
(Missing)22060
 
9.6%
ValueCountFrequency (%)
042
 
< 0.1%
0.52
 
< 0.1%
1.32
 
< 0.1%
1.410
 
< 0.1%
1.57
 
< 0.1%
1.72
 
< 0.1%
2304
0.1%
2.14
 
< 0.1%
2.2396
0.2%
2.34
 
< 0.1%
ValueCountFrequency (%)
630055
 
< 0.1%
484022
 
< 0.1%
45201
 
< 0.1%
434011
 
< 0.1%
40504
 
< 0.1%
3860200
0.1%
35201
 
< 0.1%
2880130
0.1%
270075
 
< 0.1%
2650110
< 0.1%

R_zweck
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing5509
Missing (%)2.4%
Memory size225.3 KiB
Freizeit und Unterhaltung
149802 
Arbeit und Lernen
59999 
Sonstige
15126 

Length

Max length25
Median length25
Mean length21.72278561
Min length8

Characters and Unicode

Total characters4886041
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFreizeit und Unterhaltung
2nd rowFreizeit und Unterhaltung
3rd rowFreizeit und Unterhaltung
4th rowFreizeit und Unterhaltung
5th rowSonstige

Common Values

ValueCountFrequency (%)
Freizeit und Unterhaltung149802
65.0%
Arbeit und Lernen59999
26.0%
Sonstige15126
 
6.6%
(Missing)5509
 
2.4%

Length

2022-11-25T13:18:55.123944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-25T13:18:55.235909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
und209801
32.6%
freizeit149802
23.2%
unterhaltung149802
23.2%
arbeit59999
 
9.3%
lernen59999
 
9.3%
sonstige15126
 
2.3%

Most occurring characters

ValueCountFrequency (%)
e644529
13.2%
n644529
13.2%
t524531
10.7%
419602
8.6%
r419602
8.6%
i374729
 
7.7%
u359603
 
7.4%
d209801
 
4.3%
g164928
 
3.4%
F149802
 
3.1%
Other values (11)974385
19.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4031711
82.5%
Uppercase Letter434728
 
8.9%
Space Separator419602
 
8.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e644529
16.0%
n644529
16.0%
t524531
13.0%
r419602
10.4%
i374729
9.3%
u359603
8.9%
d209801
 
5.2%
g164928
 
4.1%
a149802
 
3.7%
l149802
 
3.7%
Other values (5)389855
9.7%
Uppercase Letter
ValueCountFrequency (%)
F149802
34.5%
U149802
34.5%
A59999
13.8%
L59999
13.8%
S15126
 
3.5%
Space Separator
ValueCountFrequency (%)
419602
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4466439
91.4%
Common419602
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e644529
14.4%
n644529
14.4%
t524531
11.7%
r419602
9.4%
i374729
8.4%
u359603
8.1%
d209801
 
4.7%
g164928
 
3.7%
F149802
 
3.4%
a149802
 
3.4%
Other values (10)824583
18.5%
Common
ValueCountFrequency (%)
419602
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4886041
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e644529
13.2%
n644529
13.2%
t524531
10.7%
419602
8.6%
r419602
8.6%
i374729
 
7.7%
u359603
 
7.4%
d209801
 
4.3%
g164928
 
3.4%
F149802
 
3.1%
Other values (11)974385
19.9%

ft_abfahrt
Date

MISSING

Distinct1318
Distinct (%)0.7%
Missing38826
Missing (%)16.8%
Memory size1.8 MiB
Minimum2022-11-25 00:00:00
Maximum2022-11-25 23:59:00
2022-11-25T13:18:55.342420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:18:55.463685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ft_ankunft
Date

MISSING

Distinct1333
Distinct (%)0.7%
Missing38826
Missing (%)16.8%
Memory size1.8 MiB
Minimum2022-11-25 00:00:00
Maximum2022-11-25 23:59:00
2022-11-25T13:18:55.583426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:18:55.705986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ft_startort_uic
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct2367
Distinct (%)1.2%
Missing38826
Missing (%)16.8%
Infinite0
Infinite (%)0.0%
Mean8508835.287
Minimum1101961
Maximum8891702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-25T13:18:55.823268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1101961
5-th percentile8500023
Q18503000
median8504300
Q38507000
95-th percentile8576333
Maximum8891702
Range7789741
Interquartile range (IQR)4000

Descriptive statistics

Standard deviation51743.56461
Coefficient of variation (CV)0.006081157158
Kurtosis4640.554582
Mean8508835.287
Median Absolute Deviation (MAD)2177
Skewness-38.67778891
Sum1.630377929 × 1012
Variance2677396478
MonotonicityNot monotonic
2022-11-25T13:18:55.953061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850300020537
 
8.9%
850700011470
 
5.0%
85000107514
 
3.3%
85050006937
 
3.0%
85030165677
 
2.5%
85002185371
 
2.3%
85060004274
 
1.9%
85063023125
 
1.4%
85090002443
 
1.1%
85022042403
 
1.0%
Other values (2357)121859
52.9%
(Missing)38826
 
16.8%
ValueCountFrequency (%)
11019612
 
< 0.1%
51038651
 
< 0.1%
55100172
 
< 0.1%
800107118
< 0.1%
80010931
 
< 0.1%
80020841
 
< 0.1%
80021401
 
< 0.1%
80021813
 
< 0.1%
80022533
 
< 0.1%
80023012
 
< 0.1%
ValueCountFrequency (%)
88917021
 
< 0.1%
88120051
 
< 0.1%
87751001
 
< 0.1%
87746871
 
< 0.1%
87745493
 
< 0.1%
87715131
 
< 0.1%
87713045
< 0.1%
87182064
 
< 0.1%
859600410
< 0.1%
85959311
 
< 0.1%

ft_tu
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct55
Distinct (%)< 0.1%
Missing38826
Missing (%)16.8%
Memory size227.6 KiB
SBB
149604 
BLS
 
10586
SOB
 
7642
THU
 
6964
RhB
 
4497
Other values (50)
 
12317

Length

Max length3
Median length3
Mean length2.97306508
Min length1

Characters and Unicode

Total characters569669
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowTHU
2nd rowSBB
3rd rowSBB
4th rowSBB
5th rowSBB

Common Values

ValueCountFrequency (%)
SBB149604
64.9%
BLS10586
 
4.6%
SOB7642
 
3.3%
THU6964
 
3.0%
RhB4497
 
2.0%
ZB3431
 
1.5%
MGB1727
 
0.7%
AVA1126
 
0.5%
RBS1021
 
0.4%
AB-982
 
0.4%
Other values (45)4030
 
1.7%
(Missing)38826
 
16.8%

Length

2022-11-25T13:18:56.067116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sbb149604
78.1%
bls10586
 
5.5%
sob7642
 
4.0%
thu6964
 
3.6%
rhb4497
 
2.3%
zb3433
 
1.8%
mgb1727
 
0.9%
ava1126
 
0.6%
rbs1021
 
0.5%
ab982
 
0.5%
Other values (43)4028
 
2.1%

Most occurring characters

ValueCountFrequency (%)
B331037
58.1%
S169811
29.8%
L10713
 
1.9%
O8170
 
1.4%
U7539
 
1.3%
T7436
 
1.3%
H6964
 
1.2%
R6275
 
1.1%
h4497
 
0.8%
A4365
 
0.8%
Other values (21)12862
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter564064
99.0%
Lowercase Letter4614
 
0.8%
Dash Punctuation984
 
0.2%
Connector Punctuation7
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B331037
58.7%
S169811
30.1%
L10713
 
1.9%
O8170
 
1.4%
U7539
 
1.3%
T7436
 
1.3%
H6964
 
1.2%
R6275
 
1.1%
A4365
 
0.8%
Z4008
 
0.7%
Other values (15)7746
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
h4497
97.5%
e65
 
1.4%
r42
 
0.9%
t10
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
-984
100.0%
Connector Punctuation
ValueCountFrequency (%)
_7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin568678
99.8%
Common991
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
B331037
58.2%
S169811
29.9%
L10713
 
1.9%
O8170
 
1.4%
U7539
 
1.3%
T7436
 
1.3%
H6964
 
1.2%
R6275
 
1.1%
h4497
 
0.8%
A4365
 
0.8%
Other values (19)11871
 
2.1%
Common
ValueCountFrequency (%)
-984
99.3%
_7
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII569619
> 99.9%
None50
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B331037
58.1%
S169811
29.8%
L10713
 
1.9%
O8170
 
1.4%
U7539
 
1.3%
T7436
 
1.3%
H6964
 
1.2%
R6275
 
1.1%
h4497
 
0.8%
A4365
 
0.8%
Other values (19)12812
 
2.2%
None
ValueCountFrequency (%)
Ö49
98.0%
Ü1
 
2.0%

ft_vm
Categorical

HIGH CARDINALITY
MISSING

Distinct18455
Distinct (%)9.6%
Missing38826
Missing (%)16.8%
Memory size1.1 MiB
IC 5
 
578
IC 8
 
462
IC 8 808
 
436
IC 1
 
416
IC 8 825
 
405
Other values (18450)
189313 

Length

Max length14
Median length13
Mean length8.927101926
Min length3

Characters and Unicode

Total characters1710522
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6317 ?
Unique (%)3.3%

Sample

1st rowS 30
2nd rowIR 36 2057
3rd rowIR 75 2128
4th rowIC 8 817
5th rowS 25 8540

Common Values

ValueCountFrequency (%)
IC 5578
 
0.3%
IC 8462
 
0.2%
IC 8 808436
 
0.2%
IC 1416
 
0.2%
IC 8 825405
 
0.2%
IC 8 827402
 
0.2%
IC 8 829381
 
0.2%
IC 8 826362
 
0.2%
IC 8 810357
 
0.2%
IC 8 806338
 
0.1%
Other values (18445)187473
81.4%
(Missing)38826
 
16.8%

Length

2022-11-25T13:18:56.172795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s53333
 
11.0%
ic43944
 
9.1%
ir40063
 
8.3%
513758
 
2.8%
812727
 
2.6%
111059
 
2.3%
310258
 
2.1%
re8787
 
1.8%
756599
 
1.4%
r5507
 
1.1%
Other values (14669)277797
57.4%

Most occurring characters

ValueCountFrequency (%)
309168
18.1%
1199142
11.6%
2153441
 
9.0%
I112544
 
6.6%
5110242
 
6.4%
3103891
 
6.1%
693494
 
5.5%
888498
 
5.2%
785324
 
5.0%
R69003
 
4.0%
Other values (13)385775
22.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1010970
59.1%
Uppercase Letter323378
 
18.9%
Space Separator309168
 
18.1%
Dash Punctuation67006
 
3.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I112544
34.8%
R69003
21.3%
C65713
20.3%
S56382
17.4%
E18012
 
5.6%
G530
 
0.2%
T519
 
0.2%
V519
 
0.2%
N121
 
< 0.1%
L33
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1199142
19.7%
2153441
15.2%
5110242
10.9%
3103891
10.3%
693494
9.2%
888498
8.8%
785324
8.4%
463933
 
6.3%
057150
 
5.7%
955855
 
5.5%
Space Separator
ValueCountFrequency (%)
309168
100.0%
Dash Punctuation
ValueCountFrequency (%)
-67006
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1387144
81.1%
Latin323378
 
18.9%

Most frequent character per script

Common
ValueCountFrequency (%)
309168
22.3%
1199142
14.4%
2153441
11.1%
5110242
 
7.9%
3103891
 
7.5%
693494
 
6.7%
888498
 
6.4%
785324
 
6.2%
-67006
 
4.8%
463933
 
4.6%
Other values (2)113005
 
8.1%
Latin
ValueCountFrequency (%)
I112544
34.8%
R69003
21.3%
C65713
20.3%
S56382
17.4%
E18012
 
5.6%
G530
 
0.2%
T519
 
0.2%
V519
 
0.2%
N121
 
< 0.1%
L33
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1710522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
309168
18.1%
1199142
11.6%
2153441
 
9.0%
I112544
 
6.6%
5110242
 
6.4%
3103891
 
6.1%
693494
 
5.5%
888498
 
5.2%
785324
 
5.0%
R69003
 
4.0%
Other values (13)385775
22.6%

ft_vm_kurz
Categorical

HIGH CORRELATION
MISSING

Distinct12
Distinct (%)< 0.1%
Missing38826
Missing (%)16.8%
Memory size225.5 KiB
IC
57796 
S
56268 
IR
52053 
RE
10042 
R
6868 
Other values (7)
8583 

Length

Max length3
Median length2
Mean length1.687271019
Min length1

Characters and Unicode

Total characters323298
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowS
2nd rowIR
3rd rowIR
4th rowIC
5th rowS

Common Values

ValueCountFrequency (%)
IC57796
25.1%
S56268
24.4%
IR52053
22.6%
RE10042
 
4.4%
R6868
 
3.0%
EC5247
 
2.3%
ICE2670
 
1.2%
TGV519
 
0.2%
SN113
 
< 0.1%
IRE25
 
< 0.1%
Other values (2)9
 
< 0.1%
(Missing)38826
16.8%

Length

2022-11-25T13:18:56.278968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ic57796
30.2%
s56268
29.4%
ir52053
27.2%
re10042
 
5.2%
r6868
 
3.6%
ec5247
 
2.7%
ice2670
 
1.4%
tgv519
 
0.3%
sn113
 
0.1%
ire25
 
< 0.1%
Other values (2)9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
I112544
34.8%
R68988
21.3%
C65713
20.3%
S56382
17.4%
E17992
 
5.6%
T519
 
0.2%
G519
 
0.2%
V519
 
0.2%
N121
 
< 0.1%
L1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter323298
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I112544
34.8%
R68988
21.3%
C65713
20.3%
S56382
17.4%
E17992
 
5.6%
T519
 
0.2%
G519
 
0.2%
V519
 
0.2%
N121
 
< 0.1%
L1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin323298
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I112544
34.8%
R68988
21.3%
C65713
20.3%
S56382
17.4%
E17992
 
5.6%
T519
 
0.2%
G519
 
0.2%
V519
 
0.2%
N121
 
< 0.1%
L1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII323298
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I112544
34.8%
R68988
21.3%
C65713
20.3%
S56382
17.4%
E17992
 
5.6%
T519
 
0.2%
G519
 
0.2%
V519
 
0.2%
N121
 
< 0.1%
L1
 
< 0.1%

ft_zielort_uic
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1465
Distinct (%)0.8%
Missing38826
Missing (%)16.8%
Infinite0
Infinite (%)0.0%
Mean8500547.145
Minimum5501362
Maximum8814001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-25T13:18:56.390289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5501362
5-th percentile8500023
Q18503000
median8503126
Q38506290
95-th percentile8509000
Maximum8814001
Range3312639
Interquartile range (IQR)3290

Descriptive statistics

Standard deviation42578.66709
Coefficient of variation (CV)0.005008932527
Kurtosis613.9175755
Mean8500547.145
Median Absolute Deviation (MAD)1874
Skewness-16.82378465
Sum1.628789838 × 1012
Variance1812942891
MonotonicityNot monotonic
2022-11-25T13:18:56.508889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850300035863
 
15.6%
850700017492
 
7.6%
850500010165
 
4.4%
85002188378
 
3.6%
85000107372
 
3.2%
85060005505
 
2.4%
85063024678
 
2.0%
85030163806
 
1.7%
85090003799
 
1.6%
85021132896
 
1.3%
Other values (1455)91656
39.8%
(Missing)38826
16.8%
ValueCountFrequency (%)
55013621
 
< 0.1%
55100173
 
< 0.1%
800107115
< 0.1%
80010931
 
< 0.1%
80021401
 
< 0.1%
80021813
 
< 0.1%
80022533
 
< 0.1%
80023012
 
< 0.1%
80023071
 
< 0.1%
80023717
< 0.1%
ValueCountFrequency (%)
88140011
 
< 0.1%
87746471
 
< 0.1%
87746001
 
< 0.1%
87745496
< 0.1%
87745381
 
< 0.1%
87725681
 
< 0.1%
87723191
 
< 0.1%
87715131
 
< 0.1%
87713045
< 0.1%
87688881
 
< 0.1%

fg_abfahrt
Date

MISSING

Distinct1342
Distinct (%)0.7%
Missing27962
Missing (%)12.1%
Memory size1.8 MiB
Minimum2022-11-25 00:00:00
Maximum2022-11-25 23:59:00
2022-11-25T13:18:56.624698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:18:56.739464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

fg_ankunft
Date

MISSING

Distinct1366
Distinct (%)0.7%
Missing27962
Missing (%)12.1%
Memory size1.8 MiB
Minimum2022-11-25 00:00:00
Maximum2022-11-25 23:59:00
2022-11-25T13:18:56.858353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:18:56.974520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

fg_startort_uic
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct10855
Distinct (%)5.4%
Missing27962
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean8509955.09
Minimum1101316
Maximum8891702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-25T13:18:57.087061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1101316
5-th percentile8500026
Q18503000
median8505000
Q38507493
95-th percentile8587758
Maximum8891702
Range7790386
Interquartile range (IQR)4493

Descriptive statistics

Standard deviation108560.3254
Coefficient of variation (CV)0.01275686231
Kurtosis3628.066665
Mean8509955.09
Median Absolute Deviation (MAD)2173
Skewness-54.04320342
Sum1.723044647 × 1012
Variance1.178534424 × 1010
MonotonicityNot monotonic
2022-11-25T13:18:57.208232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850300013395
 
5.8%
85070008458
 
3.7%
85000106957
 
3.0%
85030165883
 
2.6%
85050005838
 
2.5%
85060003328
 
1.4%
85002182598
 
1.1%
85063022537
 
1.1%
85021132038
 
0.9%
85071001970
 
0.9%
Other values (10845)149472
64.9%
(Missing)27962
 
12.1%
ValueCountFrequency (%)
11013161
 
< 0.1%
11013271
 
< 0.1%
11019541
 
< 0.1%
11019571
 
< 0.1%
11020341
 
< 0.1%
11049351
 
< 0.1%
110649327
< 0.1%
14018101
 
< 0.1%
51038651
 
< 0.1%
55100172
 
< 0.1%
ValueCountFrequency (%)
88917021
 
< 0.1%
88120051
 
< 0.1%
87763021
 
< 0.1%
87751001
 
< 0.1%
87746872
 
< 0.1%
87746003
< 0.1%
87745642
 
< 0.1%
87745494
< 0.1%
87742321
 
< 0.1%
87723197
< 0.1%

fg_zielort_uic
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct10648
Distinct (%)5.3%
Missing27962
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean8507632.266
Minimum1101322
Maximum8831138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-25T13:18:57.331937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1101322
5-th percentile8500023
Q18503000
median8505000
Q38507483
95-th percentile8588581
Maximum8831138
Range7729816
Interquartile range (IQR)4483

Descriptive statistics

Standard deviation132484.0222
Coefficient of variation (CV)0.01557237291
Kurtosis2521.216593
Mean8507632.266
Median Absolute Deviation (MAD)2060
Skewness-45.87708909
Sum1.722574335 × 1012
Variance1.755201614 × 1010
MonotonicityNot monotonic
2022-11-25T13:18:57.459081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850300018400
 
8.0%
850700010195
 
4.4%
85050006997
 
3.0%
85000106783
 
2.9%
85030164943
 
2.1%
85060004166
 
1.8%
85063023589
 
1.6%
85002182731
 
1.2%
85021132502
 
1.1%
85071001971
 
0.9%
Other values (10638)140197
60.8%
(Missing)27962
 
12.1%
ValueCountFrequency (%)
11013221
 
< 0.1%
11013231
 
< 0.1%
11015351
 
< 0.1%
11018942
 
< 0.1%
11019573
 
< 0.1%
11019662
 
< 0.1%
11021382
 
< 0.1%
11025021
 
< 0.1%
110649331
< 0.1%
11100001
 
< 0.1%
ValueCountFrequency (%)
88311381
 
< 0.1%
88140011
 
< 0.1%
87747002
 
< 0.1%
87746871
 
< 0.1%
87746471
 
< 0.1%
87746001
 
< 0.1%
87745641
 
< 0.1%
87745591
 
< 0.1%
87745498
< 0.1%
87745381
 
< 0.1%

fg_startort
Categorical

HIGH CARDINALITY
MISSING

Distinct12200
Distinct (%)5.5%
Missing6747
Missing (%)2.9%
Memory size803.5 KiB
Zürich HB
 
14392
Bern
 
9061
Basel SBB
 
7562
Luzern
 
6269
Zürich Flughafen
 
6251
Other values (12195)
180154 

Length

Max length33
Median length26
Mean length11.27542704
Min length1

Characters and Unicode

Total characters2522189
Distinct characters92
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3873 ?
Unique (%)1.7%

Sample

1st rowFrauenfeld
2nd rowRheinfelden
3rd rowAmriswil
4th rowSalgesch
5th rowDottikon-Dintikon

Common Values

ValueCountFrequency (%)
Zürich HB14392
 
6.2%
Bern9061
 
3.9%
Basel SBB7562
 
3.3%
Luzern6269
 
2.7%
Zürich Flughafen6251
 
2.7%
Winterthur3627
 
1.6%
Olten2822
 
1.2%
St. Gallen2688
 
1.2%
Aarau2159
 
0.9%
Thun2089
 
0.9%
Other values (12190)166769
72.4%
(Missing)6747
 
2.9%

Length

2022-11-25T13:18:57.581171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
zürich28091
 
8.1%
hb14626
 
4.2%
bern11158
 
3.2%
basel9040
 
2.6%
sbb7845
 
2.3%
luzern7183
 
2.1%
flughafen6383
 
1.8%
st5067
 
1.5%
winterthur4990
 
1.4%
dorf4272
 
1.2%
Other values (9227)246613
71.4%

Most occurring characters

ValueCountFrequency (%)
e247729
 
9.8%
n197313
 
7.8%
r180868
 
7.2%
i138984
 
5.5%
a138547
 
5.5%
121639
 
4.8%
l120111
 
4.8%
t112420
 
4.5%
h109145
 
4.3%
s94950
 
3.8%
Other values (82)1060483
42.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1911521
75.8%
Uppercase Letter419661
 
16.6%
Space Separator121639
 
4.8%
Other Punctuation55609
 
2.2%
Dash Punctuation9934
 
0.4%
Close Punctuation1810
 
0.1%
Open Punctuation1810
 
0.1%
Decimal Number198
 
< 0.1%
Math Symbol7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e247729
13.0%
n197313
10.3%
r180868
 
9.5%
i138984
 
7.3%
a138547
 
7.2%
l120111
 
6.3%
t112420
 
5.9%
h109145
 
5.7%
s94950
 
5.0%
o79675
 
4.2%
Other values (31)491779
25.7%
Uppercase Letter
ValueCountFrequency (%)
B82021
19.5%
S48950
11.7%
Z39878
 
9.5%
H29055
 
6.9%
L23569
 
5.6%
G23212
 
5.5%
W20220
 
4.8%
A18514
 
4.4%
F16136
 
3.8%
R14731
 
3.5%
Other values (23)103375
24.6%
Decimal Number
ValueCountFrequency (%)
475
37.9%
631
15.7%
327
 
13.6%
025
 
12.6%
117
 
8.6%
212
 
6.1%
710
 
5.1%
81
 
0.5%
Other Punctuation
ValueCountFrequency (%)
,43186
77.7%
.7751
 
13.9%
/4524
 
8.1%
'147
 
0.3%
&1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
121639
100.0%
Dash Punctuation
ValueCountFrequency (%)
-9934
100.0%
Close Punctuation
ValueCountFrequency (%)
)1810
100.0%
Open Punctuation
ValueCountFrequency (%)
(1810
100.0%
Math Symbol
ValueCountFrequency (%)
+7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2331182
92.4%
Common191007
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e247729
 
10.6%
n197313
 
8.5%
r180868
 
7.8%
i138984
 
6.0%
a138547
 
5.9%
l120111
 
5.2%
t112420
 
4.8%
h109145
 
4.7%
s94950
 
4.1%
B82021
 
3.5%
Other values (64)909094
39.0%
Common
ValueCountFrequency (%)
121639
63.7%
,43186
 
22.6%
-9934
 
5.2%
.7751
 
4.1%
/4524
 
2.4%
)1810
 
0.9%
(1810
 
0.9%
'147
 
0.1%
475
 
< 0.1%
631
 
< 0.1%
Other values (8)100
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2465514
97.8%
None56675
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e247729
 
10.0%
n197313
 
8.0%
r180868
 
7.3%
i138984
 
5.6%
a138547
 
5.6%
121639
 
4.9%
l120111
 
4.9%
t112420
 
4.6%
h109145
 
4.4%
s94950
 
3.9%
Other values (60)1003808
40.7%
None
ValueCountFrequency (%)
ü41875
73.9%
ä7432
 
13.1%
ö3878
 
6.8%
è1317
 
2.3%
é1089
 
1.9%
â469
 
0.8%
Ü369
 
0.7%
Ä95
 
0.2%
ô40
 
0.1%
Ö34
 
0.1%
Other values (12)77
 
0.1%

fg_zielort
Categorical

HIGH CARDINALITY
MISSING

Distinct12048
Distinct (%)5.4%
Missing6744
Missing (%)2.9%
Memory size802.4 KiB
Zürich HB
19777 
Bern
 
10863
Luzern
 
7516
Basel SBB
 
7434
Zürich Flughafen
 
5242
Other values (12043)
172860 

Length

Max length40
Median length28
Mean length11.18388677
Min length2

Characters and Unicode

Total characters2501746
Distinct characters95
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4134 ?
Unique (%)1.8%

Sample

1st rowSeuzach
2nd rowLugano
3rd rowRichterswil
4th rowInterlaken West
5th rowBrugg AG

Common Values

ValueCountFrequency (%)
Zürich HB19777
 
8.6%
Bern10863
 
4.7%
Luzern7516
 
3.3%
Basel SBB7434
 
3.2%
Zürich Flughafen5242
 
2.3%
Winterthur4476
 
1.9%
St. Gallen3781
 
1.6%
Olten2950
 
1.3%
Aarau2686
 
1.2%
Chur2114
 
0.9%
Other values (12038)156853
68.1%
(Missing)6744
 
2.9%

Length

2022-11-25T13:18:57.704826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
zürich38145
 
10.9%
hb20146
 
5.7%
bern13773
 
3.9%
basel9074
 
2.6%
luzern8866
 
2.5%
sbb7679
 
2.2%
st6607
 
1.9%
winterthur5490
 
1.6%
flughafen5456
 
1.6%
gallen5288
 
1.5%
Other values (9203)230324
65.6%

Most occurring characters

ValueCountFrequency (%)
e234359
 
9.4%
n188545
 
7.5%
r186651
 
7.5%
i139606
 
5.6%
a138496
 
5.5%
127265
 
5.1%
l117492
 
4.7%
h114786
 
4.6%
t109174
 
4.4%
s89648
 
3.6%
Other values (85)1055724
42.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1877290
75.0%
Uppercase Letter428640
 
17.1%
Space Separator127265
 
5.1%
Other Punctuation55860
 
2.2%
Dash Punctuation8394
 
0.3%
Open Punctuation2025
 
0.1%
Close Punctuation2025
 
0.1%
Decimal Number227
 
< 0.1%
Math Symbol20
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e234359
12.5%
n188545
 
10.0%
r186651
 
9.9%
i139606
 
7.4%
a138496
 
7.4%
l117492
 
6.3%
h114786
 
6.1%
t109174
 
5.8%
s89648
 
4.8%
u77519
 
4.1%
Other values (33)481014
25.6%
Uppercase Letter
ValueCountFrequency (%)
B89334
20.8%
Z48744
11.4%
S48422
11.3%
H32815
 
7.7%
L24572
 
5.7%
G22652
 
5.3%
W18594
 
4.3%
A18009
 
4.2%
F14383
 
3.4%
R13581
 
3.2%
Other values (22)97534
22.8%
Decimal Number
ValueCountFrequency (%)
477
33.9%
330
 
13.2%
626
 
11.5%
124
 
10.6%
022
 
9.7%
221
 
9.3%
719
 
8.4%
84
 
1.8%
92
 
0.9%
52
 
0.9%
Other Punctuation
ValueCountFrequency (%)
,41438
74.2%
.8904
 
15.9%
/5397
 
9.7%
'119
 
0.2%
&2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
127265
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8394
100.0%
Open Punctuation
ValueCountFrequency (%)
(2025
100.0%
Close Punctuation
ValueCountFrequency (%)
)2025
100.0%
Math Symbol
ValueCountFrequency (%)
+20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2305930
92.2%
Common195816
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e234359
 
10.2%
n188545
 
8.2%
r186651
 
8.1%
i139606
 
6.1%
a138496
 
6.0%
l117492
 
5.1%
h114786
 
5.0%
t109174
 
4.7%
s89648
 
3.9%
B89334
 
3.9%
Other values (65)897839
38.9%
Common
ValueCountFrequency (%)
127265
65.0%
,41438
 
21.2%
.8904
 
4.5%
-8394
 
4.3%
/5397
 
2.8%
(2025
 
1.0%
)2025
 
1.0%
'119
 
0.1%
477
 
< 0.1%
330
 
< 0.1%
Other values (10)142
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2438705
97.5%
None63041
 
2.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e234359
 
9.6%
n188545
 
7.7%
r186651
 
7.7%
i139606
 
5.7%
a138496
 
5.7%
127265
 
5.2%
l117492
 
4.8%
h114786
 
4.7%
t109174
 
4.5%
s89648
 
3.7%
Other values (62)992683
40.7%
None
ValueCountFrequency (%)
ü49869
79.1%
ä6227
 
9.9%
ö3514
 
5.6%
è1287
 
2.0%
é1003
 
1.6%
â470
 
0.7%
Ü433
 
0.7%
Ä54
 
0.1%
ô53
 
0.1%
à29
 
< 0.1%
Other values (13)102
 
0.2%

ft_startort
Categorical

HIGH CARDINALITY
MISSING

Distinct6271
Distinct (%)2.9%
Missing17653
Missing (%)7.7%
Memory size628.2 KiB
Zürich HB
21526 
Bern
 
12069
Basel SBB
 
8116
Luzern
 
7366
Zürich Flughafen
 
6044
Other values (6266)
157662 

Length

Max length31
Median length27
Mean length10.29533844
Min length1

Characters and Unicode

Total characters2190673
Distinct characters88
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2882 ?
Unique (%)1.4%

Sample

1st rowFrauenfeld
2nd rowRheinfelden
3rd rowWeinfelden
4th rowVisp
5th rowDottikon-Dintikon

Common Values

ValueCountFrequency (%)
Zürich HB21526
 
9.3%
Bern12069
 
5.2%
Basel SBB8116
 
3.5%
Luzern7366
 
3.2%
Zürich Flughafen6044
 
2.6%
Olten5593
 
2.4%
Winterthur4572
 
2.0%
St. Gallen3276
 
1.4%
Chur2594
 
1.1%
Zug2534
 
1.1%
Other values (6261)139093
60.4%
(Missing)17653
 
7.7%

Length

2022-11-25T13:18:57.828635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
zürich34075
 
10.6%
hb22082
 
6.9%
bahnhof20673
 
6.5%
bern14117
 
4.4%
basel9792
 
3.1%
sbb9511
 
3.0%
luzern8575
 
2.7%
flughafen6481
 
2.0%
olten6006
 
1.9%
winterthur5836
 
1.8%
Other values (4864)183238
57.2%

Most occurring characters

ValueCountFrequency (%)
e185475
 
8.5%
n182856
 
8.3%
r150402
 
6.9%
h136880
 
6.2%
a128592
 
5.9%
B113449
 
5.2%
i112656
 
5.1%
107627
 
4.9%
l94308
 
4.3%
t84678
 
3.9%
Other values (78)893750
40.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1623837
74.1%
Uppercase Letter405788
 
18.5%
Space Separator107627
 
4.9%
Other Punctuation43820
 
2.0%
Dash Punctuation7658
 
0.3%
Open Punctuation891
 
< 0.1%
Close Punctuation891
 
< 0.1%
Decimal Number100
 
< 0.1%
Math Symbol61
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e185475
11.4%
n182856
11.3%
r150402
 
9.3%
h136880
 
8.4%
a128592
 
7.9%
i112656
 
6.9%
l94308
 
5.8%
t84678
 
5.2%
o74731
 
4.6%
u73896
 
4.6%
Other values (28)399363
24.6%
Uppercase Letter
ValueCountFrequency (%)
B113449
28.0%
Z44714
 
11.0%
S43886
 
10.8%
H33425
 
8.2%
L22281
 
5.5%
G19822
 
4.9%
W17807
 
4.4%
A14102
 
3.5%
F14096
 
3.5%
O12294
 
3.0%
Other values (23)69912
17.2%
Decimal Number
ValueCountFrequency (%)
445
45.0%
612
 
12.0%
311
 
11.0%
111
 
11.0%
710
 
10.0%
210
 
10.0%
01
 
1.0%
Other Punctuation
ValueCountFrequency (%)
,30927
70.6%
.6680
 
15.2%
/6158
 
14.1%
'31
 
0.1%
&24
 
0.1%
Space Separator
ValueCountFrequency (%)
107627
100.0%
Dash Punctuation
ValueCountFrequency (%)
-7658
100.0%
Open Punctuation
ValueCountFrequency (%)
(891
100.0%
Close Punctuation
ValueCountFrequency (%)
)891
100.0%
Math Symbol
ValueCountFrequency (%)
+61
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2029625
92.6%
Common161048
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e185475
 
9.1%
n182856
 
9.0%
r150402
 
7.4%
h136880
 
6.7%
a128592
 
6.3%
B113449
 
5.6%
i112656
 
5.6%
l94308
 
4.6%
t84678
 
4.2%
o74731
 
3.7%
Other values (61)765598
37.7%
Common
ValueCountFrequency (%)
107627
66.8%
,30927
 
19.2%
-7658
 
4.8%
.6680
 
4.1%
/6158
 
3.8%
(891
 
0.6%
)891
 
0.6%
+61
 
< 0.1%
445
 
< 0.1%
'31
 
< 0.1%
Other values (7)79
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2138544
97.6%
None52129
 
2.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e185475
 
8.7%
n182856
 
8.6%
r150402
 
7.0%
h136880
 
6.4%
a128592
 
6.0%
B113449
 
5.3%
i112656
 
5.3%
107627
 
5.0%
l94308
 
4.4%
t84678
 
4.0%
Other values (59)841621
39.4%
None
ValueCountFrequency (%)
ü42769
82.0%
ä4720
 
9.1%
ö1525
 
2.9%
è1182
 
2.3%
é889
 
1.7%
â523
 
1.0%
Ü368
 
0.7%
Ä82
 
0.2%
Ö24
 
< 0.1%
ì14
 
< 0.1%
Other values (9)33
 
0.1%

ft_zielort
Categorical

HIGH CARDINALITY
MISSING

Distinct5373
Distinct (%)2.5%
Missing17650
Missing (%)7.7%
Memory size621.2 KiB
Zürich HB
37226 
Bern
18152 
Luzern
 
10680
Olten
 
8593
Basel SBB
 
8022
Other values (5368)
130113 

Length

Max length40
Median length29
Mean length8.796241294
Min length3

Characters and Unicode

Total characters1871717
Distinct characters85
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2797 ?
Unique (%)1.3%

Sample

1st rowOberwinterthur
2nd rowZürich HB
3rd rowZürich HB
4th rowSpiez
5th rowBrugg AG

Common Values

ValueCountFrequency (%)
Zürich HB37226
 
16.2%
Bern18152
 
7.9%
Luzern10680
 
4.6%
Olten8593
 
3.7%
Basel SBB8022
 
3.5%
Winterthur5815
 
2.5%
St. Gallen4870
 
2.1%
Zürich Flughafen4105
 
1.8%
Chur3951
 
1.7%
Aarau3079
 
1.3%
Other values (5363)108293
47.0%
(Missing)17650
 
7.7%

Length

2022-11-25T13:18:57.951203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
zürich50391
 
16.7%
hb37767
 
12.5%
bern19083
 
6.3%
luzern11000
 
3.7%
olten8687
 
2.9%
basel8457
 
2.8%
sbb8215
 
2.7%
st6151
 
2.0%
winterthur6075
 
2.0%
gallen5435
 
1.8%
Other values (4514)140036
46.5%

Most occurring characters

ValueCountFrequency (%)
e165989
 
8.9%
r159031
 
8.5%
n142567
 
7.6%
i112722
 
6.0%
B101066
 
5.4%
h96956
 
5.2%
88543
 
4.7%
a85824
 
4.6%
l84307
 
4.5%
t72359
 
3.9%
Other values (75)762353
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1366695
73.0%
Uppercase Letter392651
 
21.0%
Space Separator88543
 
4.7%
Other Punctuation15786
 
0.8%
Dash Punctuation6385
 
0.3%
Open Punctuation763
 
< 0.1%
Close Punctuation763
 
< 0.1%
Decimal Number127
 
< 0.1%
Math Symbol4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e165989
12.1%
r159031
11.6%
n142567
10.4%
i112722
 
8.2%
h96956
 
7.1%
a85824
 
6.3%
l84307
 
6.2%
t72359
 
5.3%
c66773
 
4.9%
u64132
 
4.7%
Other values (27)316035
23.1%
Uppercase Letter
ValueCountFrequency (%)
B101066
25.7%
Z58776
15.0%
H44684
11.4%
S36879
 
9.4%
L22130
 
5.6%
G18450
 
4.7%
W14703
 
3.7%
O14649
 
3.7%
A13251
 
3.4%
R9729
 
2.5%
Other values (22)58334
14.9%
Decimal Number
ValueCountFrequency (%)
437
29.1%
720
15.7%
620
15.7%
320
15.7%
220
15.7%
110
 
7.9%
Other Punctuation
ValueCountFrequency (%)
.6921
43.8%
,4722
29.9%
/4121
26.1%
'20
 
0.1%
&2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
88543
100.0%
Dash Punctuation
ValueCountFrequency (%)
-6385
100.0%
Open Punctuation
ValueCountFrequency (%)
(763
100.0%
Close Punctuation
ValueCountFrequency (%)
)763
100.0%
Math Symbol
ValueCountFrequency (%)
+4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1759346
94.0%
Common112371
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e165989
 
9.4%
r159031
 
9.0%
n142567
 
8.1%
i112722
 
6.4%
B101066
 
5.7%
h96956
 
5.5%
a85824
 
4.9%
l84307
 
4.8%
t72359
 
4.1%
c66773
 
3.8%
Other values (59)671752
38.2%
Common
ValueCountFrequency (%)
88543
78.8%
.6921
 
6.2%
-6385
 
5.7%
,4722
 
4.2%
/4121
 
3.7%
(763
 
0.7%
)763
 
0.7%
437
 
< 0.1%
'20
 
< 0.1%
720
 
< 0.1%
Other values (6)76
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1807992
96.6%
None63725
 
3.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e165989
 
9.2%
r159031
 
8.8%
n142567
 
7.9%
i112722
 
6.2%
B101066
 
5.6%
h96956
 
5.4%
88543
 
4.9%
a85824
 
4.7%
l84307
 
4.7%
t72359
 
4.0%
Other values (58)698628
38.6%
None
ValueCountFrequency (%)
ü56804
89.1%
ä3227
 
5.1%
ö1019
 
1.6%
è932
 
1.5%
é693
 
1.1%
â525
 
0.8%
Ü429
 
0.7%
Ä37
 
0.1%
Ö19
 
< 0.1%
ô9
 
< 0.1%
Other values (7)31
 
< 0.1%

Interactions

2022-11-25T13:10:04.282332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:58:15.719602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:58:41.075704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:58:55.320589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:59:18.644693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:59:42.380894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:00:05.148356image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:00:33.056934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:00:54.422587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:01:23.325694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:01:53.808323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:02:14.626561image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:02:52.229014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:03:18.194892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:04:20.034040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:05:57.660793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:10:13.343583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:58:15.824587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:58:41.167328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:58:55.413270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:59:18.742614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:59:42.474752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:00:05.242230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:00:33.152055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:00:54.536189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:01:23.419223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:01:53.914437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:02:14.725599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:02:52.361647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:03:19.839450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:04:21.146924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:06:06.078074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:10:19.439488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:58:15.942359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:58:41.254530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:58:55.522120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:59:18.849494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:59:42.580508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:00:05.350991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:00:33.261853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:00:54.666265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:01:23.525050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:01:54.020351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:02:14.839617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:02:52.552122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:03:20.770641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:04:21.756464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:06:10.456420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:13:06.947174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:58:16.052041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:58:41.344974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:58:55.631908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:59:18.953275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:59:42.687570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:00:05.467603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:00:33.372585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:00:54.796178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:01:23.630647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:01:54.132804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:02:14.952118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:02:52.706038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:03:22.541076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:04:22.836542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:06:18.205647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:13:15.330848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:58:16.160434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:58:41.435006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:58:55.741106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:59:19.060948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T12:59:42.790277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-25T13:00:05.576553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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Correlations

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Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-25T13:18:58.379040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
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Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-25T13:18:58.862958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-25T13:18:59.014953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-25T13:18:43.520838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-25T13:18:44.874250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-25T13:18:47.965159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-25T13:18:48.985153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

participant_idu_dateKommentarwime_personalwime_komfortwime_sauberkeitwime_puenktlichwime_platzangebotwime_gesamtzufwime_preis_leistungwime_fahrplanwime_oes_fahrtS_spracheS_alterS_sexS_wohnsitzu_klassencodeS_AB3_HTAR_anschlussR_stoerungdevice_typedispcodeu_ticketu_fahrausweisu_preisR_zweckft_abfahrtft_ankunftft_startort_uicft_tuft_vmft_vm_kurzft_zielort_uicfg_abfahrtfg_ankunftfg_startort_uicfg_zielort_uicfg_startortfg_zielortft_startortft_zielort
06422019-01-19NoneNaN88.88888988.888889100.000000100.00000088.888889NaN100.000000100.000000DeutschNaNmännlichIn der Schweiz / Liechtenstein2. KlasseNaNJaNeinNaNNaNNaNNaNNaNFreizeit und Unterhaltung2022-11-25 17:58:002022-11-25 18:10:008506100THUS 30S85060162022-11-25 17:58:002022-11-25 18:22:0085061008506020FrauenfeldSeuzachFrauenfeldOberwinterthur
16572019-03-02None100.000000100.000000100.000000100.000000100.000000100.000000NaN100.000000100.000000Deutsch68.0männlichIn der Schweiz / Liechtenstein1. KlasseNaNJaNeinNaNNaNNaNNaNNaNFreizeit und Unterhaltung2022-11-25 05:50:002022-11-25 06:49:008500301SBBIR 36 2057IR85030002022-11-25 05:50:002022-11-25 09:18:0085003018505300RheinfeldenLuganoRheinfeldenZürich HB
2247562019-04-22NoneNaN66.66666766.666667100.00000066.66666777.777778NaN100.000000100.000000Deutsch25.0weiblichIn der Schweiz / Liechtenstein2. KlasseNaNJaNeinNaNNaNNaNNaNNaNFreizeit und Unterhaltung2022-11-25 16:36:002022-11-25 17:25:008506105SBBIR 75 2128IR85030002022-11-25 16:22:002022-11-25 18:10:0085061098503207AmriswilRichterswilWeinfeldenZürich HB
3256202019-01-05None88.88888944.44444466.666667100.00000044.44444477.777778NaN77.77777888.888889Deutsch16.0weiblichIn der Schweiz / Liechtenstein2. KlasseNaNJaNeinNaNNaNNaNNaNNaNFreizeit und Unterhaltung2022-11-25 10:57:002022-11-25 11:24:008501605SBBIC 8 817IC85074832022-11-25 10:28:002022-11-25 11:51:0085016008507493SalgeschInterlaken WestVispSpiez
4412152019-01-12NoneNaN88.88888988.888889100.000000100.00000088.888889NaN100.00000088.888889Deutsch72.0weiblichIn der Schweiz / Liechtenstein2. KlasseNaNNaNNeinNaNNaNNaNNaNNaNSonstige2022-11-25 11:10:002022-11-25 11:25:008502212SBBS 25 8540S85003092022-11-25 11:10:002022-11-25 11:25:0085022128500309Dottikon-DintikonBrugg AGDottikon-DintikonBrugg AG
5413052019-01-04Habe schon mehrmals erlebt, dass es im Speisewagen keine Gipfeli gab am Morgen. Das war jeweils ärgerlich.77.77777844.44444477.77777877.77777877.77777888.888889NaN66.66666777.777778Deutsch24.0weiblichIn der Schweiz / Liechtenstein2. KlasseNaNNaNNeinNaNNaNNaNNaNNaNArbeit und Lernen2022-11-25 16:57:002022-11-25 17:25:008502206SBBIR 75 2663IR85050002022-11-25 16:57:002022-11-25 17:25:0085022068505000BaarLuzernBaarLuzern
6413342019-02-01Ansteben, dass auch in gut frequentierte periphere Zentren eine Fahrplanverdichtung erfolgt.Die Trennung von Fernverkehr und Regionalverkehr ist in Frage zu stellen. Schade, dass ich zu meiner ersten Fahrt am Tag Stellung nehmen muss, beginne ich dich mein Reiseprogramm aus Platzgründen im Zug bewusst früher als es nötig wäre...100.00000088.888889100.000000100.00000088.88888988.888889NaN100.000000100.000000Deutsch43.0männlichIn der Schweiz / Liechtenstein2. KlasseNaNNaNNeinNaNNaNNaNNaNNaNArbeit und Lernen2022-11-25 05:50:002022-11-25 06:24:008507483SBBIC 61 1056IC85070002022-11-25 05:50:002022-11-25 06:24:0085074838507000SpiezBernSpiezBern
7413762019-02-24NoneNaN66.66666755.555556100.00000077.77777888.888889NaN66.66666777.777778Deutsch21.0weiblichIn der Schweiz / Liechtenstein2. KlasseNaNNaNNeinNaNNaNNaNNaNNaNArbeit und Lernen2022-11-25 04:56:002022-11-25 05:51:008506208SBBIR 13 3252IR85030002022-11-25 04:56:002022-11-25 05:51:0085062088503000UzwilZürich HBUzwilZürich HB
8414232019-01-06None100.000000100.000000100.000000100.000000100.000000100.000000NaN100.000000100.000000Deutsch35.0weiblichIn der Schweiz / Liechtenstein2. KlasseNaNJaNeinNaNNaNNaNNaNNaNFreizeit und Unterhaltung2022-11-25 17:41:002022-11-25 19:28:008505213SBBIC 2 884IC85030002022-11-25 17:05:002022-11-25 20:58:0085054008507000LocarnoBernBellinzonaZürich HB
9414592019-01-07Die 1. Klasse muss deutluch aufgewertet werden. Die neuen untergeordneten Haltestellen wie Zürich Altstetten und Zürich Oerlikon trüben Fahrerlebnis massiv.NaN0.00000066.66666755.55555633.33333333.333333NaN33.333333100.000000Deutsch47.0männlichIn der Schweiz / Liechtenstein1. KlasseNaNJaNeinNaNNaNNaNNaNNaNArbeit und Lernen2022-11-25 07:12:002022-11-25 08:06:008508100SBBIR 17 2359IR85030002022-11-25 07:12:002022-11-25 08:38:0085081008506000LangenthalWinterthurLangenthalZürich HB

Last rows

participant_idu_dateKommentarwime_personalwime_komfortwime_sauberkeitwime_puenktlichwime_platzangebotwime_gesamtzufwime_preis_leistungwime_fahrplanwime_oes_fahrtS_spracheS_alterS_sexS_wohnsitzu_klassencodeS_AB3_HTAR_anschlussR_stoerungdevice_typedispcodeu_ticketu_fahrausweisu_preisR_zweckft_abfahrtft_ankunftft_startort_uicft_tuft_vmft_vm_kurzft_zielort_uicfg_abfahrtfg_ankunftfg_startort_uicfg_zielort_uicfg_startortfg_zielortft_startortft_zielort
2304265899452022-11-14None100.0100.075.0100.0100.0100.075.075.075.0Deutsch75.0weiblichIn der Schweiz / Liechtenstein2. KlassejaJaNeinDesktopBeendetMobile-TicketNormales Billett18.1Freizeit und Unterhaltung2022-11-25 14:58:002022-11-25 15:57:008506302SBBIC-5-528IC85030002022-11-25 14:31:002022-11-25 15:57:0085063668503000SpeicherZürich HBSt. GallenZürich HB
2304275899462022-11-07Die WC sind leider häufig sehr sehr schmutzig. Ich fahre diese Strecke öfters.NaNNaNNaNNaNNaNNaN75.0100.0NaNDeutsch71.0weiblichIn der Schweiz / Liechtenstein2. KlassejaNaNNaNDesktopAusgescreentNaNSparbillett26.4SonstigeNaTNaTNaNNaNNaNNaNNaNNaTNaTNaNNaNLiestalChurLiestalChur
2304285899512022-11-13NoneNaN75.075.075.025.075.050.075.0100.0Deutsch45.0männlichIn der Schweiz / Liechtenstein2. KlassejaJaNeinDesktopBeendetMobile-TicketNormales Billett24.5Freizeit und Unterhaltung2022-11-25 18:04:002022-11-25 19:13:008503000SBBIC-5-532IC85043002022-11-25 18:04:002022-11-25 19:23:0085030008576724Zürich HBBiel/Bienne, OmegaZürich HBBiel/Bienne
2304295899542022-11-11None100.0100.0100.0100.0100.0100.0100.0100.0100.0Deutsch49.0männlichIn der Schweiz / Liechtenstein2. KlassejaJaNeinDesktopBeendetNaNSparbillett22.6Freizeit und Unterhaltung2022-11-25 07:02:002022-11-25 07:58:008507000SBBIC-8-807IC85030002022-11-25 06:39:002022-11-25 08:12:0085044108503016ZollikofenZürich FlughafenBernZürich HB
2304305899552022-11-11NoneNaN75.075.0100.0100.075.0100.0100.075.0Deutsch43.0männlichIn der Schweiz / Liechtenstein2. KlassejaJaNeinDesktopBeendetNaNSparbillett23.4Freizeit und Unterhaltung2022-11-25 12:31:002022-11-25 13:28:008507000SBBIC-1-717IC85030002022-11-25 11:40:002022-11-25 13:28:0085041948503000Laupen BEZürich HBBernZürich HB
2304315899562022-11-13None0.0NaNNaN100.0100.025.050.00.0100.0DeutschNaNdiversIn der Schweiz / Liechtenstein2. KlasseneinJaNeinDesktopBeendetNaNSparbillett20.2Freizeit und Unterhaltung2022-11-25 15:42:002022-11-25 16:24:008500305SBBIR-36-1977IR85030002022-11-25 15:42:002022-11-25 16:43:0085003058591183FrickZürich, HelmhausFrickZürich HB
2304325899572022-11-13NoneNaN100.0100.075.075.0100.075.0100.0100.0Deutsch48.0weiblichIn der Schweiz / Liechtenstein1. KlassejaJaNeinDesktopBeendetMobile-TicketNormales Billett57.0Freizeit und Unterhaltung2022-11-25 15:34:002022-11-25 16:58:008507100SBBIC-8-825IC85030002022-11-25 15:34:002022-11-25 17:22:0085071008503400ThunBülachThunZürich HB
2304335899592022-11-11Ich hatte meinen grossen Hund (Deutsche Dogge) dabei und war sehr zufrieden mit unserem Platz im Spielwagon unten, freie Fläche zum liegen für den Hund und dennoch eine Sitzgelegenheit für mich. Der Zugbegleiter war grandios.100.0100.075.0100.0100.0100.075.0100.0100.0Deutsch38.0weiblichIn der Schweiz / Liechtenstein2. KlasseneinJaNeinDesktopBeendetNaNSparbillett34.2Freizeit und Unterhaltung2022-11-25 09:36:002022-11-25 10:32:008507000SBBIC-61-1062IC85000102022-11-25 09:04:002022-11-25 10:56:0085041058578157NiederwangenBasel, NeuweilerstrasseBernBasel SBB
2304345899622022-11-13Die nahtlosen Anschlüsse habe ich sehr geschätzt, sodass ich rasch von Basel nach Hause kam. Sicherheitspersonal oder Kondukteure im Zug erhöhen das Sicherheitsgefühl positiv. Die Züge donnerstags abends um 22.36 Uhr sind stets überfüllt! Bitte mehr Wagen anhängen. Die Reinigung der Züge ist unterschiedlich - die Fahrgäste und ihr Verhalten wohl leider auch.NaN75.050.0100.0100.075.075.0100.075.0Deutsch61.0weiblichIn der Schweiz / Liechtenstein2. KlassejaJaNeinDesktopBeendetMobile-TicketNormales Billett12.9Freizeit und Unterhaltung2022-11-25 17:11:002022-11-25 17:57:008500010SBBIR-36-1981IR85003092022-11-25 17:11:002022-11-25 18:12:0085000108503777Basel SBBUntersiggenthal, SpiracherBasel SBBBrugg AG
2304355899652022-11-12None100.075.0100.0100.0100.0100.0100.0100.0100.0Deutsch64.0weiblichIn der Schweiz / Liechtenstein2. KlassejaJaNeinDesktopBeendetNaNSparbillett27.4Freizeit und Unterhaltung2022-11-25 09:29:002022-11-25 11:22:008502204SBBIR-70-2616IR85063022022-11-25 07:59:002022-11-25 11:22:0085082118506302SchüpfheimSt. GallenZugSt. Gallen